Voltage control method and system for solar power generation and energy storage system

Abstract

The invention discloses a voltage control method and system for a solar power generation and energy storage system. The method comprises the following steps: (1) a preparation step; (2) a test calculation and virtual power compensation step; (3) a machine learning step: training input data is the voltage value of each grid-connected node, and output data is the virtual power compensation amount of the grid-connected node; the cubic polynomial regression controllers with the same number as the energy storage systems are trained respectively, each controller uses the autonomous compensation result of each energy storage system in the area under administration of a main transformer of a secondary substation as a training data source, the operation situation is that the secondary side voltage of the main transformer is 1.0 p.u., the energy storage systems discharge, the load capacity is 0.1%-100%, and the load capacity is 0.1%-100%. The data is subjected to normalization processing before training by capturing one part of load condition data every 0.1%; and (4) an application implementation step: obtaining a cubic polynomial regression model of the solar energy and energy storage system, obtaining a required virtual power compensation amount by inputting voltage data of a power grid node and a voltage control requirement, and controlling the voltage of the system.

Description

Technical Field

[0001] The present application relates to the field of renewable energy power generation and power grid control equipment and technology, and specifically to a voltage control method and system for a solar power generation and energy storage system. Background Art

[0002] As the demand for clean energy grows, solar power generation is widely used, but its intermittent and volatile nature poses challenges to the power system. Traditional distribution systems have problems when dealing with solar power generation access: first, voltage control is difficult, and traditional methods are difficult to adapt to power changes, resulting in voltage instability; second, the energy storage system has not been fully utilized, and the control method is imperfect and cannot be coordinated with solar power generation; third, the existing power flow calculation method is not accurate and slow when dealing with such distribution systems, which affects the voltage control effect. In short, the existing distribution system has many shortcomings in terms of voltage control of solar power generation and energy storage systems.

[0003] Some voltage control methods for power equipment containing solar energy and energy storage systems have emerged, such as China's public invention patent 202211632436.3, which discloses a power storage device and control method for a solar power generation system. The device includes an electricity consumption identification module, an energy processing module, a terminal maintenance module and a server. The electricity consumption identification module synchronously obtains the first electrical data of the terminal of the energy storage device and the second electrical data of the load end, processes to obtain the total electrical data and saves it to the server; the energy processing module receives the total electrical data, processes to obtain the electrical energy value of the terminal and the load end, compares it with the preset threshold to obtain the abnormal electrical energy loss terminal and saves it to the server; the terminal maintenance module receives the abnormal electrical energy loss terminal for directional maintenance. The control method includes collecting current and voltage, processing to obtain the current mean and voltage mean, processing the voltage mean difference to determine whether the energy storage operation is abnormal, comparing the electrical energy value with the electrical energy threshold according to the abnormal signal to identify the abnormality, and completing the maintenance according to the abnormal signal. This patent can realize intelligent control of abnormalities at the terminal or load end of the energy storage device. However, this solution can mainly realize voltage control through hardware and simple automatic control equipment, and has great deficiencies in flexibility and accuracy. Summary of the invention

[0004] 1. Technical issues

[0005] The problem of complex voltage control and over-reliance on hardware under different operating conditions at grid nodes that contain both solar energy systems and energy storage systems is addressed.

[0006] (II) Technical solution

[0007] Firstly, a distribution system is constructed in which the main transformer area of ​​a secondary substation contains five high-voltage feeders with a solar power generation architecture, and 8 energy storage systems are added for virtual power compensation simulation analysis.

[0008] The rated capacity of the main transformer is 25MVA, the rated voltage is 69 / 11.4kV, the percentage impedance is 8.94%, and the X / R ratio is 31. The secondary side of the main transformer is connected to five main lines, the feeder voltage is 11.4kV, and 477AAC overhead lines are used, and underground cables are not considered. The feeder numbers are: feeder 1 (F1), feeder 2 (F2), feeder 3 (F3), feeder 4 (F4) and feeder 5 (F5), and the characteristics of each feeder are as follows:

[0009] F1: The main line is 6.75km long, with a long branch line connecting to the mountainous area, and four PVs connected to the end of the branch line.

[0010] F2: The feeder load is heavy.

[0011] F3: There are large electricity users at the end of the feeder.

[0012] F4: The feeder load is light.

[0013] F5: The feeder is long and connected to four PVs at the end.

[0014] Each feeder supplies an equivalent load at the equivalent busbar. The four busbars at the end of the F1 and F5 feeders are connected in parallel to four solar power generation systems (PV) with a rated capacity of 1.25MW, with a total PV power generation of 10MW. The PV numbers of the F1 feeder are PV1-1, PV1-2, PV1-3 and PV1-4, and the PV numbers of the F5 feeder are PV5-1, PV5-2, PV5-3 and PV5-4. The four busbars at the end of the F2 and F3 feeders are connected in parallel to four 1.25MW / 4MWh energy storage systems (BESS). The BESS numbers of the F2 feeder are BESS2-1, BESS2-2, BESS2-3, BESS2-4, BESS2-5, and the BESS numbers of the F3 feeder are BESS3-1, BESS3-2, BESS3-3, BESS3-4, BESS3-5.

[0015] After the distribution system parameters are determined, the Newton method is used to calculate the power flow and obtain the feedback voltage V t And reference voltage V ref Then, the voltage difference ΔV = V ref -V t , set the target voltage, calculate the voltage variation range ΔV1 and ΔV2 to get the control curve, and finally calculate ΔQ k .

[0016] Taking B22 bus as an example, X 22It is the sum of the reactance from the secondary side of the main transformer to the B22 bus in the power distribution system. This embodiment uses the incremental iteration method to calculate the virtual power compensation amount. The compensation amount calculated by this method has high calculation accuracy and can accurately make the bus voltage fall within the target voltage range.

[0017] That is to say, in the power distribution system, according to different voltage requirements and different power usage scenarios, each node will have different voltage responses and required virtual power compensation to meet the corresponding voltage control requirements.

[0018] The first type of voltage control requirement is the voltage control range required by the grid-connected energy storage system review specifications at the system location. To facilitate the embodiment of the inventive concept of this application, the actual regional standards are not involved here, but generally the voltage change rate is set not to exceed ±3%, and the power factor must change within 20%.

[0019] The second type of voltage control requirement is the maximum variation requirement that the stress test of the power equipment in the system can bear. Generally, the target voltage range can be set to 0.96pu-1.02pu, and the power factor can vary within 20%.

[0020] Under the above two sets of voltage control target constraints, eight voltage control scenarios are set for the above distribution system, and virtual power compensation control is performed to achieve the above two types of voltage control requirements.

[0021] In the above working scenario, the power converter in the system is controlled to adjust the phase and size of the output current to achieve virtual power compensation, so that the voltage in the system meets the above two types of voltage control requirements, thereby achieving stable control of the voltage of the solar energy and energy storage system.

[0022] The following is the specific calculation process to determine whether compensation is needed and the compensation value:

[0023] 1. Create the bus admittance matrix and import system parameters.

[0024] Import the parameters of each bus, including the bus number, initial voltage value, initial phase angle value, real / imaginary power generation and load of each bus, bus type and impedance parameters of the connecting line between the two buses, and convert the impedance matrix into an admittance matrix.

[0025] 2. Using the bus voltage, phase angle, bus admittance matrix conductance and susceptance, calculate the real power and imaginary power injection of each bus.

[0026]

[0027] Where P i Indicates the actual power injection amount of each busbar. |V i | and |V j| represent the voltage amplitude of busbars i and j respectively, G ij and B ij denote the conductance and susceptance of the bus admittance matrix, θ i and θ j They represent the voltage phase angles of bus terminals i and j respectively.

[0028] Q i Indicates the virtual power injection amount of each bus.

[0029] 3. Calculate the Jacobian matrix

[0030]

[0031] Among them J 11 (j) represents the partial derivative matrix of real power with respect to voltage phase angle;

[0032] J 12 (j) represents the partial derivative matrix of real power with respect to voltage amplitude;

[0033] J 21 (j) represents the partial derivative matrix of imaginary power with respect to voltage phase angle;

[0034] J 22 (j) represents the partial derivative matrix of imaginary power with respect to voltage amplitude;

[0035] J(j) represents the variable correction amount solved in the power flow.

[0036] 4. After subtracting the real / imaginary power net generation from the injected power, the power error is obtained.

[0037]

[0038] 5. Use the Jacobian matrix inverse matrix to calculate the variable correction

[0039]

[0040] 6. Calculate the voltage of each bus and the phase angle correction value x(j+1)

[0041]

[0042] where Δδ i (j) and |ΔV i (j)|represents the change of phase angle and voltage, δ i (j) and |V i (j)|represent the phase angle and voltage value at the last iteration respectively.

[0043] 7. According to the target voltage range, determine whether the bus voltage exceeds the range. If it exceeds the range, perform virtual power compensation. The virtual power compensation value is Where ΔV is the voltage difference and X is the reactance of the bus.

[0044] In the above scenario, the distribution system is voltage controlled under the constraints of two types of voltage control conditions, and the power parameters of each node are collected for machine learning. Specifically:

[0045] The training input data is the voltage value of each grid-connected node, and the output is the virtual power compensation amount of its grid-connected node. Eight cubic polynomial regression controllers are trained separately, and each controller uses the results of the autonomous compensation of eight energy storage systems in the jurisdiction of the secondary substation main transformer as the source of training data. The operating scenario is that the secondary side voltage of the main transformer is 1.0pu, the energy storage system is discharged, and the load capacity ranges from 0.1% to 100%. One load condition data is captured every 0.1%, for a total of 1000 load condition data. 700 of the 1000 load condition data are captured as training data, and the remaining 300 are used for testing. Before training, the data is normalized.

[0046] From the above steps, a cubic polynomial regression model of the solar energy and energy storage system is obtained, and the required virtual power compensation amount is obtained by inputting the voltage data of the grid node and the voltage control requirement.

[0047] On the other hand, the present application discloses a voltage control system of a solar power generation and energy storage system, comprising:

[0048] Preparation module for constructing a power distribution system, in which a secondary substation main transformer area includes at least five high-voltage feeders, has solar power generation units, and adds at least 8 energy storage systems for virtual power compensation simulation analysis; determine the voltage control requirements of the power distribution system;

[0049] The calculation and compensation module is used to compile at least 8 groups of main transformer secondary side voltage, feeder self-load and energy storage system operation parameters with at least one different operation scenario, and perform voltage measurement and virtual power compensation for each electrical node; the parameters for calculating the virtual power input include the input parameters of each bus, which include the bus number, voltage initial value, phase angle initial value, real / virtual power generation and load of each bus, bus type and impedance parameters of the connection line between the two buses, and convert the impedance matrix into an admittance matrix;

[0050] Machine learning module, the training input data is the voltage value of each grid-connected node, and the output is the virtual power compensation amount of its grid-connected node; the cubic polynomial regression controllers with the same number of energy storage systems are trained separately, and each controller uses the results of autonomous compensation of each energy storage system in the jurisdiction of the main transformer of the secondary substation as the training data source. The operating scenario is that the secondary side voltage of the main transformer is 1.0pu, the energy storage system is discharged, and the load capacity ranges from 0.1% to 100%. A load condition data is captured every 0.1%. Before training, the data is normalized; and

[0051] The application module is used to obtain the cubic polynomial regression model of the solar energy and energy storage system, obtain the required virtual power compensation by inputting the voltage data of the grid node and the voltage control requirements, and control the voltage of the solar energy and energy storage system.

[0052] Preferably, the calculation compensation module is specifically used for:

[0053] (1) Using the bus voltage, phase angle, bus admittance matrix conductance and susceptance, calculate the real power and imaginary power injection amount of each bus.

[0054]

[0055] Where P i Indicates the actual power injection amount of each busbar. |V i | and |V j | represent the voltage amplitude of busbars i and j respectively, G ij and B ij denote the conductance and susceptance of the bus admittance matrix, θ i and θ j denote the voltage phase angles of busbars i and j respectively;

[0056] Q i Indicates the virtual power injection amount of each bus;

[0057] (2) Calculate the Jacobian matrix:

[0058]

[0059] Among them J 11 (j) represents the partial derivative matrix of real power with respect to voltage phase angle;

[0060] J 12 (j) represents the partial derivative matrix of real power with respect to voltage amplitude;

[0061] J 21 (j) represents the partial derivative matrix of imaginary power with respect to voltage phase angle;

[0062] J 22(j) represents the partial derivative matrix of imaginary power with respect to voltage amplitude;

[0063] J(j) represents the variable correction amount solved in the power flow;

[0064] (3) After subtracting the real / imaginary power net generation from the injected power, the power error is obtained.

[0065]

[0066] (4) Use the inverse matrix of the Jacobian matrix to calculate the variable correction

[0067]

[0068] where Δδ i (j) and |ΔV i (j)|represents the change of phase angle and voltage, δ i (j) and |V i (j)|represents the phase angle and voltage value at the last iteration respectively;

[0069] (5) Calculate the voltage of each bus and the phase angle correction value x(j+1)

[0070]

[0071] where Δδ i (j) and |ΔV i (j)|represents the change of phase angle and voltage, δ i (j) and |V i (j)|represents the phase angle and voltage value at the last iteration respectively;

[0072] (6) According to the target voltage range, determine whether the bus voltage exceeds the range. If it exceeds the range, perform virtual power compensation. The virtual power compensation value is Where ΔV is the voltage difference and X is the reactance of the bus.

[0073] On the other hand, the present application also discloses an electronic device, comprising: at least one processor and a memory connected thereto; the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a solar power generation and energy storage system voltage control method as described above.

[0074] On the other hand, the present application also discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the above-mentioned voltage control method of a solar power generation and energy storage system is implemented.

[0075] (III) Beneficial effects

[0076] This solution can effectively solve the shortcomings of traditional power distribution systems in voltage control of solar power generation and energy storage systems, improve the accuracy and stability of voltage control, give full play to the role of energy storage systems, and achieve stable control of solar energy and energy storage system voltages. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A schematic diagram of the structure of a power distribution system constructed according to an implementation method of the present application;

[0078] Figure 2 It is a schematic diagram of the specific configuration of each branch according to the implementation method of this application;

[0079] Figure 3 is a block diagram of voltage-virtual power control according to an embodiment of the present application;

[0080] Figure 4 It is a flow chart of a voltage control method of a solar power generation and energy storage system according to an embodiment of the present application;

[0081] Figure 5 It is a schematic diagram of the structure of a voltage control system of a solar power generation and energy storage system according to an embodiment of the present application;

[0082] Figure 6 It is a schematic diagram of the system hardware relationship according to the implementation mode of the present application. DETAILED DESCRIPTION

[0083] The present invention will be further described below in conjunction with the embodiments.

[0084] like Figure 1 As shown, first, a distribution system is constructed, in which the main transformer area of ​​a secondary substation includes five high-voltage feeders, has a solar power generation architecture, and adds 8 energy storage systems for virtual power compensation simulation analysis.

[0085] The rated capacity of the main transformer is 25MVA, the rated voltage is 69 / 11.4kV, the percentage impedance is 8.94%, and the X / R ratio is 31. The secondary side of the main transformer is connected to five main lines, the feeder voltage is 11.4kV, and 477AAC overhead lines are used, and underground cables are not considered. The feeder numbers are: feeder 1 (F1), feeder 2 (F2), feeder 3 (F3), feeder 4 (F4) and feeder 5 (F5), and the characteristics of each feeder are as follows:

[0086] F1: The main line is 6.75km long, with a longer branch line connecting to the mountainous area, and the end of the branch line is connected to four PVs. The length of the branch line is 24.75km.

[0087] F2: The feeder load is heavy and the trunk line length is 8.1km.

[0088] F3: There is a large electricity user at the end of the feeder, and the main line length is 7.2km.

[0089] F4: The feeder load is relatively light and the trunk line length is 6.3km.

[0090] F5: The feeder is long and connected to four PV panels at the end. The main line length is 15.25 km.

[0091] Each feeder supplies an equivalent load at the equivalent busbar. The four busbars at the end of the F1 and F5 feeders are connected in parallel to four solar power generation systems (PV) with a rated capacity of 1.25MW, with a total PV power generation of 10MW. The PV numbers of the F1 feeder are PV1-1, PV1-2, PV1-3 and PV1-4, and the PV numbers of the F5 feeder are PV5-1, PV5-2, PV5-3 and PV5-4. The four busbars at the end of the F2 and F3 feeders are connected in parallel to four 1.25MW / 4MWh energy storage systems (BESS). The BESS numbers of the F2 feeder are BESS2-1, BESS2-2, BESS2-3, BESS2-4, BESS2-5, and the BESS numbers of the F3 feeder are BESS3-1, BESS3-2, BESS3-3, BESS3-4, BESS3-5.

[0092] In a well-formed power distribution system, the main parameter indicators of each feeder are as follows: Figure 2 . refer to Figure 3 After the distribution system parameters are determined, the Newton method is used to calculate the power flow and obtain the feedback voltage V t And reference voltage V ref Then, the voltage difference ΔV = V ref -V t , set the target voltage, calculate the voltage variation range ΔV1 and ΔV2 to get the control curve, and finally calculate ΔQ k .

[0093] Taking B22 bus as an example, X 22 It is the sum of the reactance from the secondary side of the main transformer to the B22 bus in the power distribution system. This embodiment uses the incremental iteration method to calculate the virtual power compensation amount. The compensation amount calculated by this method has high calculation accuracy and can accurately make the bus voltage fall within the target voltage range.

[0094] That is to say, in the power distribution system, according to different voltage requirements and different power usage scenarios, each node will have different voltage responses and required virtual power compensation to meet the corresponding voltage control requirements.

[0095] The first type of voltage control requirement is the voltage control range required by the grid-connected energy storage system review specifications at the system location. To facilitate the embodiment of the inventive concept of this application, the actual regional standards are not involved here, but generally the voltage change rate is set not to exceed ±3%, and the power factor must change within 20%.

[0096] The second type of voltage control requirement is the maximum variation requirement that the stress test of the power equipment in the system can bear. Generally, the target voltage range can be set to 0.96pu-1.02pu, and the power factor can vary within 20%.

[0097] In the above definition, pu is the abbreviation of per unit, which is the ratio obtained by comparing the actual value with the selected reference value.

[0098] Under the above two sets of voltage control target constraints, eight voltage control scenarios are set for the above distribution system, and virtual power compensation control is performed to achieve the above two types of voltage control requirements. The following are the eight manually set working scenarios of the distribution system:

[0099]

[0100] In the above working scenario, the power converter in the system is controlled to adjust the phase and size of the output current to achieve virtual power compensation, so that the voltage in the system meets the above two types of voltage control requirements, thereby achieving stable control of the voltage of the solar energy and energy storage system.

[0101] The following is the specific calculation process to determine whether compensation is needed and the compensation value:

[0102] 1. Create the bus admittance matrix and import system parameters.

[0103] Import the parameters of each bus, including the bus number, initial voltage value, initial phase angle value, real / imaginary power generation and load of each bus, bus type and impedance parameters of the connecting line between the two buses, and convert the impedance matrix into an admittance matrix.

[0104] 2. Using the bus voltage, phase angle, bus admittance matrix conductance and susceptance, calculate the real power and imaginary power injection of each bus.

[0105]

[0106] Where P i Indicates the actual power injection amount of each busbar. |V i | and |V j |represent the voltage amplitude of busbars i and j, G ij and B ij denote the conductance and susceptance of the bus admittance matrix, θi and θ j They represent the voltage phase angles of bus terminals i and j respectively.

[0107] Q i Indicates the virtual power injection amount of each bus.

[0108] 3. Calculate the Jacobian matrix

[0109]

[0110] Among them J 11 (j) represents the partial derivative matrix of real power with respect to voltage phase angle;

[0111] J 12 (j) represents the partial derivative matrix of real power with respect to voltage amplitude;

[0112] J 21 (j) represents the partial derivative matrix of imaginary power with respect to voltage phase angle;

[0113] J 22 (j) represents the partial derivative matrix of imaginary power with respect to voltage amplitude;

[0114] J(j) represents the variable correction amount solved in the power flow.

[0115] 4. After subtracting the real / imaginary power net generation from the injected power, the power error is obtained.

[0116]

[0117] 5. Use the Jacobian matrix inverse matrix to calculate the variable correction

[0118]

[0119] 6. Calculate the voltage of each bus and the phase angle correction value x(j+1)

[0120]

[0121] where Δδ i (j) and |ΔV i (j)|represents the change of phase angle and voltage, δ i (j) and |V i (j)|represent the phase angle and voltage value at the last iteration respectively.

[0122] 7. According to the target voltage range, determine whether the bus voltage exceeds the range. If it exceeds the range, perform virtual power compensation. The virtual power compensation value is Where ΔV is the voltage difference and X is the reactance of the bus.

[0123] In the above scenario, the distribution system is voltage controlled under the constraints of two types of voltage control conditions, and the power parameters of each node are collected for machine learning. Specifically:

[0124] The training input data is the voltage value of each grid-connected node, and the output is the virtual power compensation amount of its grid-connected node. Eight cubic polynomial regression controllers are trained separately, and each controller uses the results of the autonomous compensation of eight energy storage systems in the jurisdiction of the secondary substation main transformer as the source of training data. The operating scenario is that the secondary side voltage of the main transformer is 1.0pu, the energy storage system is discharged, and the load capacity ranges from 0.1% to 100%. One load condition data is captured every 0.1%, for a total of 1000 load condition data. 700 of the 1000 load condition data are captured as training data, and the remaining 300 are used for testing. Before training, the data is normalized.

[0125] The so-called normalization process refers to the process of The data is scaled to the range between 0 and 1, where x is the original data and x min and x max are the minimum and maximum values ​​of the data.

[0126] The so-called polynomial regression controller refers to a mechanism that can perform regression analysis based on input data and control a system or process. The "cubic polynomial" refers to a mathematical expression form, generally y = β0 + β1x + β2x 2 +β3x 3 , where y is the target variable, x is the input variable, and β0, β1, β2, and β3 are coefficients to be determined. The cubic polynomial regression controller is a machine learning model that uses a cubic polynomial regression model to learn the variation pattern of input training data and fits the result with a cubic curve.

[0127] From the above steps, a cubic polynomial regression model of the solar energy and energy storage system is obtained, and the required virtual power compensation amount is obtained by inputting the voltage data of the grid node and the voltage control requirement.

[0128] Figure 4 4 is a flow chart of a voltage control method for a solar power generation and energy storage system according to an embodiment of the present application. The voltage control method 400 includes:

[0129] Preparation step 401: constructing a power distribution system, wherein a main transformer area of ​​a secondary substation includes at least five high-voltage feeders, has a solar power generation unit, and adds at least 8 energy storage systems for virtual power compensation simulation analysis; determining the voltage control requirements of the power distribution system;

[0130] Test calculation and virtual power compensation step 402: compile at least 8 sets of main transformer secondary side voltage, feeder self-load and energy storage system operation parameters with at least one different operation scenario, and perform voltage measurement and virtual power compensation for each electrical node; calculate the parameters of virtual power input including inputting parameters of each bus, which parameters include bus number, voltage initial value, phase angle initial value, real / virtual power generation and load of each bus, bus type and impedance parameters of the connection line between two buses, and convert the impedance matrix into an admittance matrix;

[0131] Machine learning step 403: the training input data is the voltage value of each grid-connected node, and the output is the virtual power compensation amount of the grid-connected node; the cubic polynomial regression controllers with the same number as the energy storage systems are trained separately, and each controller uses the results of the autonomous compensation of each energy storage system in the jurisdiction of the main transformer of the secondary substation as the training data source. The operating scenario is that the secondary side voltage of the main transformer is 1.0pu, the energy storage system is discharged, and the load capacity ranges from 0.1% to 100%. A load condition data is captured every 0.1%. Before training, the data is normalized;

[0132] Application implementation step 404: obtain a cubic polynomial regression model of the solar energy and energy storage system, obtain the required virtual power compensation amount by inputting the voltage data of the grid node and the voltage control requirement, and control the voltage of the solar energy and energy storage system.

[0133] Figure 5 Schematic diagram of the structure of the voltage control system of the solar power generation and energy storage system according to the embodiment of the present application. The voltage control system 500 includes:

[0134] Preparation module 501 is used to construct a power distribution system, in which a main transformer area of ​​a secondary substation includes at least five high-voltage feeders, has a solar power generation unit, and adds at least 8 energy storage systems for virtual power compensation simulation analysis; determine the voltage control requirements of the power distribution system;

[0135] The calculation and compensation module 502 is used to compile at least 8 groups of main transformer secondary side voltage, feeder self-load and energy storage system operation parameters with at least one different operation scenario, and perform voltage measurement and virtual power compensation for each electrical node; the parameters for calculating the virtual power input include the input of each bus parameter, which includes the bus number, voltage initial value, phase angle initial value, real / virtual power generation and load of each bus, bus type and impedance parameters of the connection line between two buses, and convert the impedance matrix into an admittance matrix;

[0136] Machine learning module 503, the training input data is the voltage value of each grid-connected node, and the output is the virtual power compensation amount of its grid-connected node; the cubic polynomial regression controllers with the same number as the energy storage systems are trained separately, and each controller uses the results of autonomous compensation of each energy storage system in the jurisdiction of the main transformer of the secondary substation as the training data source. The operating scenario is that the secondary side voltage of the main transformer is 1.0pu, the energy storage system is discharged, and the load capacity ranges from 0.1% to 100%. A load condition data is captured every 0.1%. Before training, the data is normalized; and

[0137] Application module 504 is used to obtain a cubic polynomial regression model of the solar energy and energy storage system, obtain the required virtual power compensation amount by inputting the voltage data of the grid node and the voltage control requirement, and control the voltage of the solar energy and energy storage system.

[0138] like Figure 6 FIG. 1 is a schematic diagram of hardware relationships of a voltage control method for a solar power generation and energy storage system provided in accordance with an embodiment of the present invention.

[0139] The electronic device may include a processor, a memory, and may also include a computer program stored in the memory and run on the processor. In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor is the control core (Control Unit) of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory, and calls the data stored in the memory to perform various functions of the electronic device and process data.

[0140] The memory includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory may also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the electronic device. Furthermore, the memory may also include both an internal storage unit of the electronic device and an external storage device.

[0141] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0142] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, WiFi modules, etc., which will not be repeated here.

[0143] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A voltage control method for a solar power generation and energy storage system, characterized in that: The method comprises (i) a preparation step: constructing a power distribution system, wherein a main transformer area of ​​a secondary substation includes at least five high-voltage feeders, has a solar power generation unit, and adds at least eight energy storage systems for virtual power compensation simulation analysis; determining voltage control requirements of the power distribution system; (ii) Test calculation and virtual power compensation steps: compile at least 8 sets of main transformer secondary side voltage, feeder self-load and energy storage system operating parameters with at least one different operating scenario, and perform voltage measurement and virtual power compensation for each electrical node; calculate the parameters of virtual power input including the parameters of each bus, which include bus number, voltage initial value, phase angle initial value, real / virtual power generation and load of each bus, bus type and impedance parameters of the connecting line between two buses, and convert the impedance matrix into an admittance matrix; (III) Machine learning steps: the training input data is the voltage value of each grid-connected node, and the output is the virtual power compensation amount of its grid-connected node; The cubic polynomial regression controllers with the same number as the energy storage systems are trained separately. Each controller uses the results of autonomous compensation of each energy storage system in the main transformer area of ​​the secondary substation as the training data source. The operating scenario is that the secondary side voltage of the main transformer is 1.0pu, the energy storage system is discharged, and the load capacity ranges from 0.1% to 100%. Before training, a load condition data is captured every 0.1%. The data is normalized; (IV) Application implementation steps: Obtain a cubic polynomial regression model of the solar energy and energy storage system, obtain the required virtual power compensation by inputting the voltage data of the grid nodes and the voltage control requirements, and control the voltage of the solar energy and energy storage system.

2. The voltage control method of solar power generation and energy storage system according to claim 1, characterized in that: The test calculation and virtual power compensation steps include: (1) Using the bus voltage, phase angle, bus admittance matrix conductance and susceptance, calculate the real power and imaginary power injection amount of each bus. Where P i Indicates the actual power injection amount of each busbar. |V i | and |V j | represent the voltage amplitude of busbars i and j respectively, G ij and B ij denote the conductance and susceptance of the bus admittance matrix, θ i and θ j denote the voltage phase angles of busbars i and j respectively; Q i Indicates the virtual power injection amount of each bus; (2) Calculate the Jacobian matrix: Among them J 11 (j) represents the partial derivative matrix of real power with respect to voltage phase angle; J 12 (j) represents the partial derivative matrix of real power with respect to voltage amplitude; J 21 (j) represents the partial derivative matrix of imaginary power with respect to voltage phase angle; J 22 (j) represents the partial derivative matrix of imaginary power with respect to voltage amplitude; J(j) represents the variable correction amount solved in the power flow; (3) After subtracting the real / imaginary power net generation from the injected power, the power error is obtained. (4) Use the inverse matrix of the Jacobian matrix to calculate the variable correction where Δδ i (j) and |ΔV i (j)|represents the change of phase angle and voltage, δ i (j) and |V i (j)|represents the phase angle and voltage value at the last iteration respectively; (5) Calculate the voltage of each bus and the phase angle correction value x(j+1) where Δδ i (j) and |ΔV i (j)|represents the change of phase angle and voltage, δ i (j) and |V i (j)|represents the phase angle and voltage value at the last iteration respectively; (6) According to the target voltage range, determine whether the bus voltage exceeds the range. If it exceeds the range, perform virtual power compensation. The virtual power compensation value is Where ΔV is the voltage difference and X is the reactance of the bus.

3. The voltage control method of the solar power generation and energy storage system according to claim 2, characterized in that: The rated capacity of the main transformer in the distribution system is 25MVA, the rated voltage is 69 / 11.4kV, the percentage impedance is 8.94%, and the X / R ratio is 31; the secondary side of the main transformer is connected to five main lines, the feeder voltage is 11.4kV, and the feeder numbers are: feeder one (F1), feeder two (F2), feeder three (F3), feeder four (F4) and feeder five (F5); each feeder supplies an equivalent load on the equivalent bus, and the four buses at the end of the F1 and F5 feeders are each connected in parallel with four solar power generation systems (PV) with a rated capacity of 1.25MW, and the total PV power generation is 10MW. The PV numbers of the F1 feeder are PV1-1, PV1-2, PV1-3 and PV1-4, and the PV numbers of the F5 feeder are PV5-1, PV5-2, PV5-3 and PV5-4. The four buses at the end of the F2 and F3 feeders are each connected in parallel to four 1.25MW / 4MWh energy storage systems (BESS). The BESSs on the F2 feeder are numbered BESS2-1, BESS2-2, BESS2-3, BESS2-4, and BESS2-5, and the BESSs on the F3 feeder are numbered BESS3-1, BESS3-2, BESS3-3, BESS3-4, and BESS3-5.

4. The voltage control method of the solar power generation and energy storage system according to claim 3, characterized in that: The operation scenarios of the test calculation and virtual power compensation steps include at least: Scenario 1: The energy storage system does not need to perform virtual power compensation, and the bus voltage meets the target voltage of 0.96pu-1.02pu; In scenario 2, when the energy storage system performs virtual power compensation, the voltage will be suppressed so that the voltage falls back to the target voltage range of 0.96pu-1.02pu, and the power factor of the energy storage system meets the specifications; Scenario 3: When the energy storage system performs virtual power compensation, it will increase the voltage so that the voltage falls back to the target voltage range of 0.96pu-1.02pu, and the power factor of the energy storage system meets the specifications; Scenario 4: When the energy storage system performs virtual power compensation, the voltage will be increased so that the voltage falls back to the target voltage range of 0.96pu-1.02pu, and the power factor of the energy storage system meets the specifications; Scenario 5: The energy storage system does not need to perform virtual power compensation, and the bus voltage meets the target voltage of 0.96pu-1.02pu; Scenario 6: When the energy storage system performs virtual power compensation, the voltage will be suppressed so that the voltage falls back to the target voltage range of 0.96pu-1.02pu, and the power factor of the energy storage system meets the specifications; Scenario 7: When the energy storage system performs virtual power compensation, it will increase the voltage so that the voltage falls back to the target voltage range of 0.96pu-1.02pu, and the power factor of the energy storage system meets the specifications; In scenario eight, the energy storage system does not need to perform virtual power compensation, and the bus voltage meets the target voltage of 0.96pu-1.02pu.

5. The voltage control method of the solar power generation and energy storage system according to claim 4, characterized in that: The voltage control requirements of the distribution system in the preparation step include two types of requirements. The first type of voltage control requirement is the voltage control range required by the grid-connected review specifications for the energy storage system at the system location; the second type of voltage control requirement is the maximum variation requirement that can be borne by the stress test of the power equipment in the system.

6. The voltage control method of the solar power generation and energy storage system according to claim 5, characterized in that: In the machine learning step, the so-called normalization process refers to the process of The data is scaled to the range between 0 and 1, where x is the original data and x min and x max are the minimum and maximum values ​​of the data.

7. A voltage control system for a solar power generation and energy storage system, characterized in that: include: Preparation module for constructing a power distribution system, in which a secondary substation main transformer area includes at least five high-voltage feeders, has solar power generation units, and adds at least 8 energy storage systems for virtual power compensation simulation analysis; determine the voltage control requirements of the power distribution system; The calculation and compensation module is used to compile at least 8 groups of main transformer secondary side voltage, feeder self-load and energy storage system operation parameters with at least one different operation scenario, and perform voltage measurement and virtual power compensation for each electrical node; the parameters for calculating the virtual power input include the input parameters of each bus, which include the bus number, voltage initial value, phase angle initial value, real / virtual power generation and load of each bus, bus type and impedance parameters of the connection line between the two buses, and convert the impedance matrix into an admittance matrix; Machine learning module, the training input data is the voltage value of each grid-connected node, and the output is the virtual power compensation amount of its grid-connected node; the cubic polynomial regression controllers with the same number of energy storage systems are trained separately, and each controller uses the results of autonomous compensation of each energy storage system in the jurisdiction of the main transformer of the secondary substation as the source of training data. The operating scenario is that the secondary side voltage of the main transformer is 1.0pu, the energy storage system is discharged, and the load capacity ranges from 0.1% to 100%. A load condition data is captured every 0.1%. Before training, the data is normalized; as well as The application module is used to obtain the cubic polynomial regression model of the solar energy and energy storage system, obtain the required virtual power compensation by inputting the voltage data of the grid node and the voltage control requirements, and control the voltage of the solar energy and energy storage system.

8. The voltage control system of the solar power generation and energy storage system according to claim 7, characterized in that: The calculation compensation module is specifically used for: (1) Using the bus voltage, phase angle, bus admittance matrix conductance and susceptance, calculate the real power and imaginary power injection amount of each bus. Where P i Indicates the actual power injection amount of each busbar. |V i | and |V j | represent the voltage amplitude of busbars i and j respectively, G ij and B ij denote the conductance and susceptance of the bus admittance matrix, θ i and θ j denote the voltage phase angles of busbars i and j respectively; Q i Indicates the virtual power injection amount of each bus; (2) Calculate the Jacobian matrix: Among them J 11 (j) represents the partial derivative matrix of real power with respect to voltage phase angle; J 12 (j) represents the partial derivative matrix of real power with respect to voltage amplitude; J 21 (j) represents the partial derivative matrix of imaginary power with respect to voltage phase angle; J 22 (j) represents the partial derivative matrix of imaginary power with respect to voltage amplitude; J(j) represents the variable correction amount solved in the power flow; (3) After subtracting the real / imaginary power net generation from the injected power, the power error is obtained. (4) Use the inverse matrix of the Jacobian matrix to calculate the variable correction where Δδ i (j) and |ΔV i (j)|represents the change of phase angle and voltage, δ i (j) and |V i (j)|represents the phase angle and voltage value at the last iteration respectively; (5) Calculate the voltage of each bus and the phase angle correction value x(j+1) where Δδ i (j) and |ΔV i (j)|represents the change of phase angle and voltage, δ i (j) and |V i (j)|represents the phase angle and voltage value at the last iteration respectively; (6) According to the target voltage range, determine whether the bus voltage exceeds the range. If it exceeds the range, perform virtual power compensation. The virtual power compensation value is Where ΔV is the voltage difference and X is the reactance of the bus.

9. An electronic device, characterized in that: The electronic device comprises: At least one processor and a memory connected thereto; the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a voltage control method for a solar power generation and energy storage system as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a voltage control method for a solar power generation and energy storage system as described in any one of claims 1 to 6 is implemented.

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