A voltage control method and system for a solar power generation and energy storage system

Abstract

This application discloses a voltage control method and system for a solar power generation and energy storage system. The method includes: (i) a preparation step; (ii) a test calculation and virtual power compensation step; (iii) a machine learning step: 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; a cubic polynomial regression controller with the same number of energy storage systems is 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 source of training data. The operating scenario is that the secondary side voltage of the main transformer is 1.0 pu, the energy storage system is discharging, and the load is from 0.1% to 100%. Load condition data is captured in 0.1% increments. Before training, the data is normalized; (iv) an application implementation step: the cubic polynomial regression model of the solar power and energy storage system is obtained. The required virtual power compensation amount is obtained by inputting the voltage data of the grid nodes and the voltage control requirements, and the voltage of the system is controlled.

Description

Technical Field

[0001] This application relates to the fields of new energy power generation and grid control equipment and technology, specifically to a voltage control method and system for a solar power generation and energy storage system. Background Technology

[0002] With the increasing demand for clean energy, solar power generation is widely used, but its intermittent and fluctuating nature poses challenges to the power system. Traditional distribution systems face several problems when integrating solar power: first, voltage control is difficult, and traditional methods struggle to adapt to voltage instability caused by power variations; second, energy storage systems are not fully utilized, and inadequate control methods prevent them from coordinating with solar power generation; third, existing power flow calculation methods are inaccurate and slow when dealing with such distribution systems, affecting voltage control effectiveness. In short, existing distribution systems have many shortcomings in voltage control for both solar power generation and energy storage systems.

[0003] Several voltage control methods for power equipment containing solar energy and energy storage systems have emerged. For example, Chinese invention patent 202211632436.3 discloses an energy storage device and control method for a solar power generation system. This device includes an electricity consumption identification module, an energy processing module, a terminal maintenance module, and a server. The electricity consumption identification module simultaneously acquires first electrical data from the energy storage device terminal and second electrical data from the load end, processes it to obtain total electrical data, and saves it to the server. The energy processing module receives the total electrical data, processes it to obtain the energy values ​​of the terminal and load end, compares them with preset thresholds to identify terminals with abnormal energy loss, and saves the data to the server. The terminal maintenance module receives terminals with abnormal energy loss and performs targeted maintenance. The control method includes collecting current and voltage, processing them to obtain average current and average voltage, processing the difference between the average voltage values ​​to determine if the energy storage operation is abnormal, comparing the energy value with the energy threshold to identify abnormalities based on abnormal signals, and completing maintenance based on the abnormal signals. This patent enables intelligent management and control of abnormalities at the energy storage device terminal or load end. However, this solution is mainly capable of controlling voltage with hardware and simple automatic control equipment, and its flexibility and accuracy are quite limited. Summary of the Invention

[0004] (I) Technical Issues

[0005] This addresses the issues of complex voltage control and over-reliance on hardware in grid nodes that simultaneously contain both solar and energy storage systems under different operating conditions.

[0006] (II) Technical Solution

[0007] First, a power 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 incorporates eight energy storage systems for virtual power compensation simulation analysis.

[0008] The main transformer has a rated capacity of 25MVA, a rated voltage of 69 / 11.4kV, a percentage impedance of 8.94%, and an X / R ratio of 31. The secondary side of the main transformer is connected to five main lines, with a feeder voltage of 11.4kV. These are 477AAC overhead lines; 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). 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 the end of the branch line connects to four PVs.

[0010] F2: The feeder has a heavy load.

[0011] F3: Large electricity users are located at the end of the feeder line.

[0012] F4: The feeder load is relatively light.

[0013] F5: The feeder is relatively long and the end is connected to four PV units.

[0014] Each feeder supplies an equivalent load on its equivalent busbar. The four busbars at the very end of feeders F1 and F5 each connect to four 1.25MW rated solar power systems (PV), for a total PV power generation of 10MW. The PVs on feeder F1 are numbered PV1-1, PV1-2, PV1-3, and PV1-4, and those on feeder F5 are numbered PV5-1, PV5-2, PV5-3, and PV5-4. The four busbars at the very end of feeders F2 and F3 each connect to four 1.25MW / 4MWh battery energy storage systems (BESS). The BESSs on feeder F2 are numbered BESS2-1, BESS2-2, BESS2-3, BESS2-4, and BESS2-5, and those on feeder F3 are numbered BESS3-1, BESS3-2, BESS3-3, BESS3-4, and BESS3-5.

[0015] After the power distribution system parameters are determined, the feedback voltage V is first obtained by calculating the power flow using Newton's method. t and reference voltage V ref Then, the voltage difference ΔV = V is calculated. ref -V t Set the target voltage, calculate the voltage change ranges ΔV1 and ΔV2 to obtain the control curve, and finally calculate ΔQ. k .

[0016] Taking bus B22 as an example, X 22This is the sum of the reactance from the secondary side of the main transformer to busbar B22 in the power distribution system. This embodiment uses an incremental iterative method to calculate the virtual power compensation. This method yields a high-precision compensation, ensuring the busbar voltage falls precisely within the target transformer range.

[0017] In other words, in this power distribution system, depending on different voltage demands and different power consumption scenarios, each node will have different voltage responses and require 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 connection review specifications for energy storage systems in the local area of ​​the system. To facilitate the presentation of the inventive concept of this application, the actual regional standards are not involved here. Instead, the voltage fluctuation rate is generally set to not exceed ±3%, and the power factor should vary within 20%.

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

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

[0021] In the above working scenario, by adjusting the phase and magnitude of the output current through the power converter in the control system, virtual power compensation is achieved, so that the voltage in the system meets the above two types of voltage control requirements, thereby realizing stable control of the voltage of the solar energy and energy storage system.

[0022] The following is the detailed calculation process for determining whether compensation is needed and the compensation amount:

[0023] 1. Establish the bus admittance matrix and the parameters of the incoming system.

[0024] Input the parameters of each bus, which include bus number, initial voltage value, initial phase angle 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.

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

[0026]

[0027] Where P i This indicates the actual power injection amount for each bus. |V i | and | V j| represent the voltage amplitudes of bus i and j respectively, G ij and B ij Let θ represent the conductance and susceptance of the bus admittance matrix, respectively. i and θ j These represent the voltage phase angles of bus non-i and j, respectively.

[0028] Q i This indicates the amount of virtual power injected into each bus.

[0029] 3. Calculate the Jacobian matrix

[0030]

[0031] 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 virtual power with respect to voltage phase angle;

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

[0035] J(j) represents the variable correction amount obtained from the power flow calculation.

[0036] 4. The power error is obtained by subtracting the net power generation from the injection amount.

[0037]

[0038] 5. Calculate the variable correction amount using the inverse of the Jacobian matrix.

[0039]

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

[0041]

[0042] Where Δδ i (j) and |ΔV i (j)| represents the changes in phase angle and voltage, respectively, δ i (j) and |V i (j)| represents the phase angle and voltage value at the previous iteration, respectively.

[0043] 7. Based on the target voltage range, determine if the bus voltage exceeds the range. If it does, 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-described scenario, voltage control is performed on the power distribution system under two types of voltage control conditions, and the power parameters of each node are collected for machine learning. Specifically:

[0045] The training input data consists of the voltage values ​​of each grid-connected node, and the output is the virtual power compensation amount of that node. Eight cubic polynomial regression controllers are trained separately. Each controller uses the results of autonomous compensation from eight energy storage systems within the main transformer area of ​​the secondary substation as its training data source. The operating scenario is a main transformer secondary voltage of 1.0 pu, energy storage system discharge, and load ranging from 0.1% to 100%. One load condition data point is captured for every 0.1%, resulting in 1000 load condition data points. 700 data points are selected from these 1000 as training data, and the remaining 300 are used for testing. The data is normalized before training.

[0046] The above steps yield a cubic polynomial regression model for the solar and energy storage system. The required virtual power compensation amount is obtained by inputting the voltage data and voltage control requirements of the grid nodes.

[0047] On the other hand, this application discloses a voltage control system for a solar power generation and energy storage system, including:

[0048] The preparation module is used to construct a power distribution system in which the main transformer area of ​​a secondary substation includes at least five high-voltage feeders, has a solar power generation unit, and incorporates at least eight energy storage systems for virtual power compensation simulation analysis; and to determine the voltage control requirements of the power distribution system.

[0049] The calculation and compensation module is used to generate at least 8 sets of operating scenarios where at least one of the secondary side voltage of the main transformer, feeder self-load, and energy storage system operating parameters is different, and to measure the voltage of each electrical node and perform virtual power compensation. The parameters for calculating virtual power input include the parameters of each bus, which include bus number, initial voltage value, initial phase angle value, actual / virtual power generation and load of each bus, bus type, and impedance parameters of the connecting line between two buses, and converts the impedance matrix into an admittance matrix.

[0050] The machine learning module takes the voltage values ​​of each grid-connected node as input and outputs the virtual power compensation amount of that node. Each cubic polynomial regression controller, with the same number of energy storage systems, is trained individually. Each controller uses the autonomous compensation results of each energy storage system within the main transformer area of ​​the secondary substation as its training data source. The operating scenario is a main transformer secondary voltage of 1.0 pu, energy storage system discharge, and load ranging from 0.1% to 100%. Load condition data is captured in 0.1% increments before training, and the data is normalized.

[0051] The application module is used to obtain a cubic polynomial regression model of the solar and energy storage system. By inputting the voltage data of the grid nodes and the voltage control requirements, it obtains the required virtual power compensation amount and controls the voltage of the solar and energy storage system.

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

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

[0054]

[0055] Where P i This indicates the actual power injection amount for each bus. |V i | and | V j | represent the voltage amplitudes of bus i and j respectively, G ij and B ij Let θ represent the conductance and susceptance of the bus admittance matrix, respectively. i and θ j These represent the voltage phase angles of bus non-i and j, respectively;

[0056] Q i This indicates the virtual power injection amount for each bus;

[0057] (2) Calculate the Jacobian matrix:

[0058]

[0059] 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 virtual power with respect to voltage phase angle;

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

[0063] J(j) represents the variable correction amount obtained from the power flow calculation;

[0064] (3) The power error is obtained by subtracting the net power generation from the injection amount.

[0065]

[0066] (4) Calculate the variable correction amount using the inverse of the Jacobian matrix.

[0067]

[0068] Where Δδ i (j) and |ΔV i (j)| represents the changes in phase angle and voltage, respectively, δ i (j) and |V i (j)| represents the phase angle and voltage value in the previous iteration, respectively;

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

[0070]

[0071] Where Δδ i (j) and |ΔV i (j)| represents the changes in phase angle and voltage, respectively, δ i (j) and |V i (j)| represents the phase angle and voltage value in the previous iteration, respectively;

[0072] (6) Based on the target voltage range, determine whether the bus voltage exceeds the range. If it does, 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, this application also discloses an electronic device, which includes: at least one processor and a memory connected thereto; the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform a voltage control method for a solar power generation and energy storage system as described above.

[0074] On the other hand, this application also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described voltage control method for a solar power generation and energy storage system.

[0075] (III) Beneficial Effects

[0076] This solution effectively addresses the shortcomings of traditional power distribution systems in voltage control of solar power generation and energy storage systems, improves the accuracy and stability of voltage control, fully leverages the role of energy storage systems, and achieves stable voltage control for both solar power and energy storage systems. Attached Figure Description

[0077] Figure 1 This is a schematic diagram of a power distribution system structure constructed according to the embodiments of this application;

[0078] Figure 2 This is a schematic diagram illustrating the specific configuration of each branch according to the embodiments of this application;

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

[0080] Figure 4 This is a flowchart of a voltage control method for a solar power generation and energy storage system according to an embodiment of this application;

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

[0082] Figure 6 This is a schematic diagram of the system hardware relationship according to an embodiment of this application. Detailed Implementation

[0083] The present invention will be further described below with reference to the embodiments.

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

[0085] The main transformer has a rated capacity of 25MVA, a rated voltage of 69 / 11.4kV, a percentage impedance of 8.94%, and an X / R ratio of 31. The secondary side of the main transformer is connected to five main lines, with a feeder voltage of 11.4kV. These are 477AAC overhead lines; 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). The characteristics of each feeder are as follows:

[0086] F1: The main line is 6.75km long, with a long branch line connecting to the mountainous area. The branch line ends at four PV units, with a total branch line length of 24.75km.

[0087] F2: The feeder has a heavy load and the main line is 8.1km long.

[0088] F3: The feeder ends at a major electricity user, and the main line is 7.2km long.

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

[0090] F5: The feeder is relatively long and connects to four PV units at the end, with a trunk line length of 15.25km.

[0091] Each feeder supplies an equivalent load on its equivalent busbar. The four busbars at the very end of feeders F1 and F5 each connect to four 1.25MW rated solar power systems (PV), for a total PV power generation of 10MW. The PVs on feeder F1 are numbered PV1-1, PV1-2, PV1-3, and PV1-4, and those on feeder F5 are numbered PV5-1, PV5-2, PV5-3, and PV5-4. The four busbars at the very end of feeders F2 and F3 each connect to four 1.25MW / 4MWh battery energy storage systems (BESS). The BESSs on feeder F2 are numbered BESS2-1, BESS2-2, BESS2-3, BESS2-4, and BESS2-5, and those on feeder F3 are numbered BESS3-1, BESS3-2, BESS3-3, BESS3-4, and BESS3-5.

[0092] In a well-constructed power distribution system, the main parameters of each feeder are as follows: Figure 2 . refer to Figure 3 The voltage-virtual power control block diagram is used to calculate the feedback voltage V after the power distribution system parameters are determined. This is done by first using Newton's method to calculate the power flow. t and reference voltage V ref Then, the voltage difference ΔV = V is calculated. ref -V t Set the target voltage, calculate the voltage change ranges ΔV1 and ΔV2 to obtain the control curve, and finally calculate ΔQ. k .

[0093] Taking bus B22 as an example, X 22 This is the sum of the reactance from the secondary side of the main transformer to busbar B22 in the power distribution system. This embodiment uses an incremental iterative method to calculate the virtual power compensation. This method yields a high-precision compensation, ensuring the busbar voltage falls precisely within the target transformer range.

[0094] In other words, in this power distribution system, depending on different voltage demands and different power consumption scenarios, each node will have different voltage responses and require 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 connection review specifications for energy storage systems in the local area of ​​the system. To facilitate the presentation of the inventive concept of this application, the actual regional standards are not involved here. Instead, the voltage fluctuation rate is generally set to not exceed ±3%, and the power factor should vary within 20%.

[0096] The second type of voltage control requirement is the maximum variation that the power equipment in the system can withstand during pressure testing. 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 an abbreviation for per unit, which is the ratio obtained by comparing the actual value with a selected reference value.

[0098] Under the constraints of the two voltage control targets mentioned above, eight voltage control scenarios are set for the aforementioned power distribution system, and virtual power compensation control is implemented to achieve the two types of voltage control requirements. The following are the eight manually set operating scenarios for the power distribution system:

[0099]

[0100] In the above working scenario, by adjusting the phase and magnitude of the output current through the power converter in the control system, virtual power compensation is achieved, so that the voltage in the system meets the above two types of voltage control requirements, thereby realizing stable control of the voltage of the solar energy and energy storage system.

[0101] The following is the detailed calculation process for determining whether compensation is needed and the compensation amount:

[0102] 1. Establish the bus admittance matrix and the parameters of the incoming system.

[0103] Input the parameters of each bus, which include bus number, initial voltage value, initial phase angle 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.

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

[0105]

[0106] Where P i This indicates the actual power injection amount for each bus. |V i | and | V j | represent the voltage amplitudes of bus i and j respectively, G ij and B ij Let θ represent the conductance and susceptance of the bus admittance matrix, respectively.i and θ j These represent the voltage phase angles of bus non-i and j, respectively.

[0107] Q i This indicates the amount of virtual power injected into each bus.

[0108] 3. Calculate the Jacobian matrix

[0109]

[0110] 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 virtual power with respect to voltage phase angle;

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

[0114] J(j) represents the variable correction amount obtained from the power flow calculation.

[0115] 4. The power error is obtained by subtracting the net power generation from the injection amount.

[0116]

[0117] 5. Calculate the variable correction amount using the inverse of the Jacobian matrix.

[0118]

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

[0120]

[0121] Where Δδ i (j) and |ΔV i (j)| represents the changes in phase angle and voltage, respectively, δ i (j) and |V i (j)| represents the phase angle and voltage value at the previous iteration, respectively.

[0122] 7. Based on the target voltage range, determine if the bus voltage exceeds the range. If it does, 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-described scenario, voltage control is performed on the power distribution system under two types of voltage control conditions, and the power parameters of each node are collected for machine learning. Specifically:

[0124] The training input data consists of the voltage values ​​of each grid-connected node, and the output is the virtual power compensation amount of that node. Eight cubic polynomial regression controllers are trained separately. Each controller uses the results of autonomous compensation from eight energy storage systems within the main transformer area of ​​the secondary substation as its training data source. The operating scenario is a main transformer secondary voltage of 1.0 pu, energy storage system discharge, and load ranging from 0.1% to 100%. One load condition data point is captured for every 0.1%, resulting in 1000 load condition data points. 700 data points are selected from these 1000 as training data, and the remaining 300 are used for testing. The data is normalized before training.

[0125] The so-called normalization process refers to the process of normalizing the process by using the formula... The data is scaled to a range between 0 and 1, where x is the original data, x... min and x max These 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. "Cubic polynomial" refers to a mathematical expression, 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. A cubic polynomial regression controller is a machine learning model that uses a cubic polynomial regression model to learn the changing patterns of the input training data and fits the results to a cubic curve.

[0127] The above steps yield a cubic polynomial regression model for the solar and energy storage system. The required virtual power compensation amount is obtained by inputting the voltage data and voltage control requirements of the grid nodes.

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

[0129] Preparation step 401: Construct a power distribution system in which the main transformer area of ​​a certain secondary substation includes at least five high-voltage feeders, has a solar power generation unit, and incorporates at least eight energy storage systems for virtual power compensation simulation analysis; determine the voltage control requirements of the power distribution system;

[0130] Test calculation and virtual power compensation step 402: Compile at least 8 sets of operating scenarios with at least one different value for the secondary side voltage of the main transformer, the self-load of the feeder, and the operating parameters of the energy storage system, and perform voltage measurement and virtual power compensation for each electrical node; calculate the parameters for virtual power input, including the parameters of each bus, which include the bus number, initial voltage value, initial phase angle value, actual / 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;

[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 its grid-connected node; the cubic polynomial regression controllers with the same number of 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 source of training data. The operating scenario is that the secondary side voltage of the main transformer is 1.0 pu, the energy storage system is discharging, and the load is from 0.1% to 100%. Load condition data is captured in 0.1% increments. Before training, the data is normalized.

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

[0133] Figure 5 This is a schematic diagram of the voltage control system of a solar power generation and energy storage system according to an embodiment of this application. The voltage control system 500 includes:

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

[0135] The calculation and compensation module 502 is used to compile at least 8 sets of operating scenarios in which at least one of the secondary side voltage of the main transformer, feeder self-load, and energy storage system operating parameters is different, and to measure the voltage of each electrical node and perform virtual power compensation. The parameters for calculating virtual power input include the parameters of each bus, which include bus number, initial voltage value, initial phase angle value, actual / virtual power generation and load of each bus, bus type, and impedance parameters of the connecting line between two buses, and converts the impedance matrix into an admittance matrix.

[0136] Machine learning module 503 takes the voltage values ​​of each grid-connected node as input for training and outputs the virtual power compensation amount of that node. Each cubic polynomial regression controller, with the same number of energy storage systems, is trained individually. Each controller uses the results of autonomous compensation from each energy storage system within the main transformer area of ​​the secondary substation as its training data source. The operating scenario is a main transformer secondary voltage of 1.0 pu, energy storage system discharge, and load ranging from 0.1% to 100%. Load condition data is captured in 0.1% increments before training, and the data is normalized.

[0137] Application module 504 is used to obtain a cubic polynomial regression model of the solar and energy storage system. By inputting the voltage data of the grid nodes and the voltage control requirements, it obtains the required virtual power compensation amount and controls the voltage of the solar and energy storage system.

[0138] like Figure 6 The diagram shown is a hardware relationship diagram of a voltage control method for a solar power generation and energy storage system provided in an embodiment of the present invention.

[0139] The electronic device may include a processor, a memory, and a computer program stored in the memory and executable on the processor. In some embodiments, the processor may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor is the control unit of the electronic device, connecting various components of the device via various interfaces and lines. It executes programs or modules stored in the memory and calls data stored in the memory to perform various functions and process data.

[0140] The memory includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory may be an external storage device of the electronic device, such as a plug-in portable hard drive, SmartMediaCard (SMC), Secure Digital (SD) card, FlashCard, etc. Furthermore, the memory may include both internal and external storage units of the electronic 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, or combine certain components, or have different component arrangements.

[0142] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, WiFi modules, etc., which will not be elaborated further here.

[0143] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A voltage control method for a solar power generation and energy storage system, characterized in that: include (I) Preparation steps: Construct a power distribution system in which the main transformer area of ​​a certain secondary substation contains 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; (II) Test Calculation and Virtual Power Compensation Steps: Compile at least 8 sets of operating scenarios with at least one different value for the secondary side voltage of the main transformer, the self-load of the feeder, and the operating parameters of the energy storage system. Perform voltage measurement and virtual power compensation for each electrical node. The parameters for calculating the virtual power input include the parameters of each busbar. The parameters of each busbar include the busbar number, initial voltage value, initial phase angle value, actual / virtual power generation and load of each busbar, busbar type, and impedance parameters of the connecting line between two busbars. Then, 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 same number of cubic polynomial regression controllers as the energy storage systems were trained separately. Each controller used 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 was that the secondary side voltage of the main transformer was 1.0 pu, the energy storage system was discharging, and the load ranged from 0.1% to 100%. Load condition data was captured in 0.1% increments. Before training, the data was normalized. (iv) Application and implementation steps: Obtain the 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 nodes and the voltage control requirements, and control the voltage of the solar energy and energy storage system.

2. The voltage control method for a 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 virtual power injection of each bus. Where P i Indicates the actual power injection amount of each bus; |V i | and | V j | represent the voltage amplitudes of bus i and j respectively, G ij and B ij Let θ represent the conductance and susceptance of the bus admittance matrix, respectively. i and θ j These represent the voltage phase angles of bus i and j, respectively; Q i This indicates the virtual power injection amount for each bus; (2) Calculate the Jacobian matrix: 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 virtual power with respect to voltage phase angle; J 22 (j) represents the partial derivative matrix of virtual power with respect to voltage magnitude; J(j) represents the variable correction amount obtained from the power flow calculation; (3) The power error is obtained by subtracting the net power generation from the injection amount. (4) Calculate the variable correction amount using the inverse of the Jacobian matrix. Where Δδ i (j) and |ΔV i (j)| represents the changes in phase angle and voltage, respectively, δ i (j) and |V i (j)| represents the phase angle and voltage value in the previous iteration, respectively; (5) Calculate the voltage magnitude and phase angle correction value x(j+1) for each bus. Where Δδ i (j) and |ΔV i (j)| represents the changes in phase angle and voltage, respectively, δ i (j) and |V i (j)| represents the phase angle and voltage value in the previous iteration, respectively; (6) Based on the target voltage range, determine whether the bus voltage exceeds the range. If it does, 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 for a solar power generation and energy storage system according to claim 2, characterized in that: The main transformer in the power distribution system has a rated capacity of 25MVA, a rated voltage of 69 / 11.4kV, a percentage impedance of 8.94%, and an X / R ratio of 31. The secondary side of the main transformer is connected to five main lines, with a feeder voltage of 11.4kV. The feeder numbers are: Feeder 1 (F1), Feeder 2 (F2), Feeder 3 (F3), Feeder 4 (F4), and Feeder 5 (F5). Each feeder supplies an equivalent load on an equivalent busbar. The four busbars at the very end of feeders F1 and F5 are each connected in parallel to four solar power generation systems (PV) with a rated capacity of 1.25MW each. The total PV power generation is 10MW. The PV number on feeder F1 is... The PV numbers for feeder F5 are PV1-1, PV1-2, PV1-3, and PV1-4, respectively. The PV numbers for feeder F5 are PV5-1, PV5-2, PV5-3, and PV5-4, respectively. The four busbars at the very end of feeders F2 and F3 are each connected to four 1.25MW / 4MWh energy storage systems (BESS). The BESS numbers for feeder F2 are BESS2-1, BESS2-2, BESS2-3, BESS2-4, and BESS2-5, respectively. The BESS numbers for feeder F3 are BESS3-1, BESS3-2, BESS3-3, BESS3-4, and BESS3-5, respectively.

4. The voltage control method for a solar power generation and energy storage system according to claim 1, characterized in that: The voltage control requirements for the power distribution system in the preparation steps include two types of requirements. The first type of voltage control requirement is the voltage control range required by the grid connection review specifications for the energy storage system in the system's location. The second type of voltage control requirement is the maximum variation that the power equipment in the system can withstand during pressure testing.

5. The voltage control method for a solar power generation and energy storage system according to claim 4, characterized in that: In the machine learning step, the normalization process refers to the process of applying the formula... The data is scaled to a range between 0 and 1, where x is the original data, x... mi and x max These are the minimum and maximum values ​​of the data.

6. A voltage control system for a solar power generation and energy storage system, characterized in that: include: The preparation module is used to construct a power distribution system in which the main transformer area of ​​a secondary substation includes at least five high-voltage feeders, has a solar power generation unit, and incorporates at least eight energy storage systems for virtual power compensation simulation analysis; and to determine the voltage control requirements of the power distribution system. The calculation and compensation module is used to generate at least 8 sets of operating scenarios where at least one of the secondary side voltage of the main transformer, feeder self-load, and energy storage system operating parameters is different, and to measure the voltage of each electrical node and perform virtual power compensation. The parameters for calculating virtual power input include the parameters of each busbar, which include busbar number, initial voltage value, initial phase angle value, actual / virtual power generation and load of each busbar, busbar type, and impedance parameters of the connecting line between two busesbars, and converts the impedance matrix into an admittance matrix. The machine learning module takes voltage values ​​of each grid-connected node as training input and outputs virtual power compensation for its grid-connected node. The same number of cubic polynomial regression controllers as the energy storage systems were trained separately. Each controller used 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 was that the secondary side voltage of the main transformer was 1.0 pu, the energy storage system was discharging, and the load ranged from 0.1% to 100%. Load condition data was captured in 0.1% increments. Before training, the data was normalized. as well as The application module is used to obtain a cubic polynomial regression model of the solar and energy storage system. By inputting the voltage data of the grid nodes and the voltage control requirements, it obtains the required virtual power compensation amount and controls the voltage of the solar and energy storage system.

7. The voltage control system of the solar power generation and energy storage system according to claim 6, characterized in that, The calculation and compensation module is specifically used for: (1) Using the bus voltage, phase angle, bus admittance matrix conductance and susceptance, calculate the real power and virtual power injection of each bus. Where P i Indicates the actual power injection amount of each bus; |V i | and | V j | represent the voltage amplitudes of bus i and j respectively, G ij and B ij Let θ represent the conductance and susceptance of the bus admittance matrix, respectively. i and θ j These represent the voltage phase angles of bus i and j, respectively; Q i This indicates the virtual power injection amount for each bus; (2) Calculate the Jacobian matrix: 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 virtual power with respect to voltage phase angle; J 22 (j) represents the partial derivative matrix of virtual power with respect to voltage magnitude; J(j) represents the variable correction amount obtained from the power flow calculation; (3) The power error is obtained by subtracting the net power generation from the injection amount. (4) Calculate the variable correction amount using the inverse of the Jacobian matrix. Where Δδ i (j) and |ΔV i (j)| represents the changes in phase angle and voltage, respectively, δ i (j) and |V i (j)| represents the phase angle and voltage value in the previous iteration, respectively; (5) Calculate the voltage magnitude and phase angle correction value x(j+1) for each bus. Where Δδ i (j) and |ΔV i (j)| represents the changes in phase angle and voltage, respectively, δ i (j) and |Vi i (j)| represents the phase angle and voltage value in the previous iteration, respectively; (6) Based on the target voltage range, determine whether the bus voltage exceeds the range. If it does, perform virtual power compensation. The virtual power compensation value is... Where ΔV is the voltage difference and X is the reactance of the bus.

8. An electronic device, characterized in that, The electronic device includes: At least one processor and a memory connected thereto; the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a voltage control method for a solar power generation and energy storage system as described in any one of claims 1-5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a voltage control method for a solar power generation and energy storage system as described in any one of claims 1-5.

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