Distributed energy grid-connected control device for power system

Abstract

The invention relates to the technical field of distributed energy grid-connected control in an electric power system, and discloses a distributed energy grid-connected control device for the electric power system, which comprises a direct current side control unit and an inverter side control unit, the direct current side control unit is connected with a photovoltaic array and a battery energy storage device, and adopts a photovoltaic load shedding and battery voltage stabilization strategy to maintain the stability of the voltage of a direct current bus; and the inverter side control unit is connected with the inverter, and the output power and frequency of the inverter are accurately controlled by adopting a virtual synchronous machine control strategy of inertia damping adaptive MPC, so that the stability and the electric energy quality of a power system are improved. According to the device provided by the invention, by adopting a photovoltaic load shedding and battery voltage stabilization strategy on a direct current side and adopting an inertia damping self-adaptive MPC virtual synchronous machine control strategy on an inverter side, the problems of stability and electric energy quality when a distributed energy power generation system is accessed to a power grid are solved; and the supporting capacity and the adaptive capacity of the distributed energy power generation system to the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy grid connection control in power systems, and particularly to a distributed energy grid connection control device for a power system. More specifically, it relates to a distributed energy grid connection control device and method that adopts photovoltaic load shedding and battery voltage stabilization strategies, as well as virtual synchronous generator control with inertia damping adaptive MPC, aiming to improve the stability and power quality when a distributed energy generation system is connected to the grid. Background Art

[0002] With the increasing global demand for clean energy, distributed energy sources such as solar photovoltaic power generation have been widely applied and developed rapidly. However, distributed energy generation systems have characteristics such as intermittency, volatility, and uncertainty. After large-scale connection to the grid, they will bring many challenges to the stability, power quality, and dispatching operation of the grid.

[0003] On the DC side, the output power of the photovoltaic array will fluctuate violently with the changes of environmental factors such as light intensity and temperature. This may lead to unstable DC bus voltage, which in turn affects the normal operation of the inverter and power quality. Although traditional maximum power point tracking (MPPT) algorithms can make the photovoltaic array work near the maximum power point in most cases, they cannot flexibly adjust the output power according to the actual needs of the grid and lack the ability to support the grid frequency and voltage. At the same time, when the output power of the photovoltaic array is excessive, it may cause energy waste and even damage to grid equipment.

[0004] On the inverter side, distributed energy generation systems usually use power electronic inverters to connect to the grid. The inverter itself does not have the inertia and damping characteristics of traditional synchronous generators and cannot respond naturally to changes in grid frequency and power like synchronous generators. When a grid fault or disturbance occurs, the distributed energy generation system may lose synchronization with the grid, leading to system collapse and seriously threatening the safe and stable operation of the grid. To solve this problem, virtual synchronous generator (VSG) technology has emerged. It simulates the operating characteristics of synchronous generators through control algorithms, enabling the inverter to have a certain inertia and damping, thereby improving the compatibility and stability of the distributed energy generation system with the grid. However, most existing VSG control strategies adopt fixed inertia coefficients and damping coefficients and cannot be adaptively adjusted according to the real-time operating conditions of the grid and load changes, resulting in limited control effects under different operating conditions.

[0005] Therefore, a new distributed energy grid connection control technology is needed that can effectively cope with the fluctuations in the output power of the photovoltaic array on the DC side, maintain the stability of the DC bus voltage, and at the same time achieve precise control of the inverter output power and frequency on the inverter side, improve the inertia and damping characteristics of the distributed energy generation system, and enhance its support and adaptability to the grid. Summary of the Invention

[0006] The object of the present invention is to provide a distributed energy grid-connected control device for a power system. By adopting a photovoltaic load shedding and battery voltage stabilization strategy on the DC side and an inertia damping adaptive MPC virtual synchronous machine control strategy on the inverter side, the stability and power quality problems when a distributed energy generation system is connected to the grid are solved, and the support ability and adaptability of the distributed energy generation system to the grid are improved.

[0007] To achieve the above object, the present invention provides a distributed energy grid-connected control device for a power system, which is characterized in that it includes a DC side control unit and an inverter side control unit; the DC side control unit is connected to a photovoltaic array and a battery energy storage device, and adopts a photovoltaic load shedding and battery voltage stabilization strategy to maintain the stability of the DC bus voltage; the inverter side control unit is connected to an inverter, and adopts an inertia damping adaptive MPC virtual synchronous machine control strategy to achieve precise control of the output power and frequency of the inverter, so as to improve the stability and power quality of the power system.

[0008] Preferably, the photovoltaic load shedding strategy is specifically as follows: based on the real-time monitored environmental parameters such as light intensity and temperature, as well as the change of the grid frequency, by adjusting the operating point of the photovoltaic array to deviate from the maximum power point operation, a certain amount of active power reserve is reserved; the adjustment process adopts an adaptive variable step size maximum power point tracking (MPPT) algorithm to quickly and accurately adjust the output power of the photovoltaic array.

[0009] Preferably, the adaptive variable step size MPPT algorithm includes the following steps:

[0010] (1) Real-time collect the voltage and current data of the photovoltaic array, and calculate the current output power;

[0011] (2) Dynamically adjust the step size factor according to the grid frequency deviation and the light intensity change rate;

[0012] (3) Update the reference voltage of the photovoltaic array based on the adjusted step size factor, so that the operating point of the photovoltaic array moves towards the target power point.

[0013] Preferably, the battery voltage stabilization strategy is specifically as follows: connect the battery energy storage device and the DC bus through a bidirectional DC-DC converter, and real-time monitor the DC bus voltage; when the DC bus voltage is higher than the set upper limit value, control the bidirectional DC-DC converter to charge the battery energy storage device and absorb the excess energy; when the DC bus voltage is lower than the set lower limit value, control the bidirectional DC-DC converter to discharge the battery energy storage device and release energy to maintain the stability of the DC bus voltage; the bidirectional DC-DC converter adopts a proportional-integral-derivative (PID) controller for closed-loop control.

[0014] Preferably, the parameters of the PID controller are adaptively adjusted in real time according to the state of charge (SOC) of the battery, the charge and discharge current, and the fluctuations of the DC bus voltage, so as to improve the accuracy and response speed of the battery voltage stabilization control.

[0015] Preferably, the virtual synchronous machine control strategy of the inertia damping adaptive MPC includes the following steps:

[0016] (1) Establish a mathematical model of the virtual synchronous machine to simulate the inertia and damping characteristics of the synchronous generator;

[0017] (2) Adopt the model predictive control (MPC) algorithm to predict the system state in a future period according to the feedback information such as the grid frequency deviation and the inverter output power;

[0018] (3) Based on the prediction results, adjust the inertia coefficient and damping coefficient of the virtual synchronous machine in real time to adapt to different grid operating conditions and load changes.

[0019] Preferably, the objective function of the MPC algorithm comprehensively considers the frequency deviation, power deviation, and the change rates of the inertia coefficient and damping coefficient, and takes minimizing the objective function as the optimization goal to solve the optimal control input; the weight coefficients of the objective function are dynamically adjusted according to the operating state and stability requirements of the power grid.

[0020] Preferably, the method for adjusting the inertia coefficient and damping coefficient of the virtual synchronous machine in real time is as follows: when the grid frequency fluctuates greatly, increase the inertia coefficient to improve the inertia response ability of the system, and at the same time increase the damping coefficient to suppress the oscillation of the system; when the grid frequency fluctuates slightly, decrease the inertia coefficient and damping coefficient to improve the response speed and efficiency of the system.

[0021] Preferably, the device further includes a communication unit, which is used to realize information interaction with the power grid dispatching center, other distributed energy generation units, and the load side; receive power grid dispatching instructions through the communication unit, and obtain the operating states and load demand information of other generating units to optimize the operating strategy of the distributed energy grid-connected control device.

[0022] Preferably, the communication unit combines wireless communication technology and wired communication technology to ensure the reliability and real-time performance of communication; the wireless communication technology includes but is not limited to Wi-Fi, ZigBee, 4G / 5G, etc., and the wired communication technology includes but is not limited to Ethernet, optical fiber communication, etc.

[0023] The technical solution of the present invention has the following beneficial effects compared with the prior art:

[0024] (1) Improve the stability of the DC bus voltage: Through the synergistic effect of the PV load shedding and battery voltage regulation strategies, it can effectively cope with the fluctuations in the output power of the PV array, maintain the DC bus voltage within a stable range, provide good conditions for the normal operation of the inverter, and improve the power quality.

[0025] (2) Enhance the inertia and damping characteristics of the distributed energy generation system: Adopt the virtual synchronous machine control strategy with inertia damping adaptive MPC, so that the inverter has the inertia and damping characteristics similar to those of a synchronous generator, and can adaptively adjust the inertia coefficient and damping coefficient according to the real-time working conditions of the power grid and load changes, improve the response ability of the distributed energy generation system to power grid frequency and power changes, and enhance the stability of the power grid.

[0026] (3) Improve the energy utilization efficiency and the adaptability to the power grid: The PV load shedding strategy enables the PV array to flexibly adjust the output power according to the actual demand of the power grid, reserve spare power, and improve the energy utilization efficiency. At the same time, the setting of the communication unit enables the distributed energy grid connection control device to interact with the power grid dispatching center and other power generation units, optimize the operation strategy, and improve the adaptability to the power grid. Specific implementation manners

[0027] The following details the specific implementation manners of the present invention. It should be understood that the specific implementation manners described herein are only for explaining and illustrating the present invention, and are not used to limit the present invention.

[0028] According to the technical solution of the present invention, the DC side control strategy:

[0029] (1) PV load shedding strategy

[0030] The PV load shedding strategy of the present invention is based on real-time monitored environmental parameters such as light intensity and temperature, as well as the change of grid frequency. By adjusting the operating point of the PV array to deviate from the maximum power point operation, a certain amount of active power reserve is reserved. Specifically, an adaptive variable step-size maximum power point tracking (MPPT) algorithm is used to achieve PV load shedding. This algorithm first collects the voltage and current data of the PV array in real time and calculates the current output power. Then, the step-size factor is dynamically adjusted according to the grid frequency deviation and the light intensity change rate. When the grid frequency drops or the light intensity changes rapidly, the step-size factor is appropriately increased to quickly adjust the output power of the PV array and provide more active power support; when the grid frequency is stable and the light intensity changes little, the step-size factor is decreased to improve the accuracy of power adjustment and avoid over-adjustment. Finally, based on the adjusted step-size factor, the reference voltage of the PV array is updated to move the operating point of the PV array towards the target power point. In this way, the PV array can flexibly adjust the output power according to the actual needs of the grid, not only providing additional active power when the grid needs it, but also reserving a certain amount of standby power when the light is sufficient, improving the energy utilization efficiency and the stability of the grid.

[0031] (2) Battery voltage stabilization strategy

[0032] The battery voltage stabilization strategy connects the battery energy storage device and the DC bus through a bidirectional DC-DC converter and monitors the DC bus voltage in real time. When the DC bus voltage is higher than the set upper limit value, the bidirectional DC-DC converter is controlled to charge the battery energy storage device to absorb the excess energy; when the DC bus voltage is lower than the set lower limit value, the bidirectional DC-DC converter is controlled to discharge the battery energy storage device to release energy to maintain the stability of the DC bus voltage. The bidirectional DC-DC converter adopts a proportional-integral-derivative (PID) controller for closed-loop control. To improve the accuracy and response speed of the battery voltage stabilization control, the parameters of the PID controller are adjusted in real time adaptively according to the state of charge (SOC) of the battery, the charge and discharge current, and the fluctuation of the DC bus voltage. For example, when the battery SOC is high, the control gain of the charging current is appropriately reduced to avoid overcharging; when the DC bus voltage fluctuates greatly, the proportional coefficient of the controller is increased to speed up the response speed.

[0033] According to the technical solution of the present invention, the control strategy on the inverter side:

[0034] The virtual synchronous machine control strategy of inertia damping adaptive MPC:

[0035] The virtual synchronous machine control strategy of the inertia damping adaptive MPC of the present invention includes the following steps. First, establish a mathematical model of the virtual synchronous machine to simulate the inertia and damping characteristics of the synchronous generator. This model considers the rotor motion equation, electromagnetic power equation, etc. By controlling the output voltage and current of the inverter, the inverter is made to have dynamic response characteristics similar to those of the synchronous generator. Then, adopt the model predictive control (MPC) algorithm. According to feedback information such as grid frequency deviation and inverter output power, predict the system state in the future for a period of time. The objective function of the MPC algorithm comprehensively considers the frequency deviation, power deviation, and the change rates of the inertia coefficient and damping coefficient, and takes minimizing the objective function as the optimization goal to solve the optimal control input. The weight coefficients of the objective function are dynamically adjusted according to the operating state and stability requirements of the power grid. For example, when the grid frequency fluctuates greatly, increase the weight coefficient of the frequency deviation to give priority to ensuring the stability of the frequency; when the power fluctuates greatly, increase the weight coefficient of the power deviation to improve the accuracy of power control. Finally, based on the prediction results, adjust the inertia coefficient and damping coefficient of the virtual synchronous machine in real time to adapt to different grid conditions and load changes. When the grid frequency fluctuates greatly, increase the inertia coefficient to improve the inertia response ability of the system, and at the same time increase the damping coefficient to suppress the oscillation of the system; when the grid frequency fluctuates little, reduce the inertia coefficient and damping coefficient to improve the response speed and efficiency of the system.

[0036] (3) According to the technical solution of the present invention, the communication unit:

[0037] The distributed energy grid connection control device of the present invention further includes a communication unit for realizing information interaction with the power grid dispatching center, other distributed energy generation units, and the load side. Receive power grid dispatching instructions through the communication unit, obtain the operating states and load demand information of other generating units, so as to optimize the operating strategy of the distributed energy grid connection control device. The communication unit adopts a combination of wireless communication technology and wired communication technology to ensure the reliability and real-time nature of communication. Wireless communication technologies include but are not limited to Wi-Fi, ZigBee, 4G / 5G, etc., and wired communication technologies include but are not limited to Ethernet, optical fiber communication, etc.

[0038] According to the technical solution of the present invention, hardware implementation:

[0039] The hardware part of the distributed energy grid connection control device of the present invention mainly includes the following modules:

[0040] The sensor module: used to collect parameters such as the voltage, current, light intensity, and temperature of the photovoltaic array in real time, parameters such as the voltage, current, and SOC of the battery, and parameters such as the frequency, voltage, and power of the power grid. The sensor module adopts high-precision and high-reliability sensors to ensure the accuracy and real-time nature of data collection.

[0041] Control Module: A high-performance microprocessor or digital signal processor (DSP) is used as the control core to implement the virtual synchronous machine control algorithm for PV load shedding, battery voltage regulation, and inertia damping adaptive MPC. The control module has powerful computing and data processing capabilities, can quickly respond to the data collected by sensors, and output corresponding control signals according to the control algorithm.

[0042] Power Conversion Module: It includes a bidirectional DC-DC converter and an inverter. The bidirectional DC-DC converter is used to achieve energy conversion between the battery energy storage device and the DC bus, and the inverter is used to convert DC electrical energy into AC electrical energy and feed it into the grid. The power conversion module adopts advanced power electronic devices and topologies to improve energy conversion efficiency and reliability.

[0043] Communication Module: It realizes information interaction with the grid dispatching center, other distributed energy generation units, and the load side. The communication module supports multiple communication protocols and communication methods, such as Modbus, TCP / IP, Wi-Fi, 4G / 5G, etc., to ensure the reliability and real-time performance of communication.

[0044] According to the technical solution of the present invention, software implementation:

[0045] The software part of the control module mainly includes the following program modules:

[0046] Data Acquisition and Processing Module: It is responsible for receiving the data collected by the sensor module, and performing processing such as filtering and calibration to provide accurate data input for the subsequent control algorithm.

[0047] PV Load Shedding Control Module: It implements an adaptive variable-step MPPT algorithm, dynamically adjusts the operating point of the PV array according to the grid frequency deviation and the change rate of light intensity, and realizes PV load shedding.

[0048] Battery Voltage Regulation Control Module: According to parameters such as the DC bus voltage and the battery SOC, it controls the operating state of the bidirectional DC-DC converter to achieve the charge and discharge control of the battery and the stability of the DC bus voltage.

[0049] Inertia Damping Adaptive MPC Control Module: It establishes a mathematical model of the virtual synchronous machine, uses the MPC algorithm to predict the system state, and adjusts the inertia coefficient and damping coefficient of the virtual synchronous machine in real time to achieve precise control of the inverter output power and frequency.

[0050] Communication Management Module: It is responsible for communicating with the grid dispatching center, other distributed energy generation units, and the load side, receiving and sending control instructions and operation data, and realizing information interaction and sharing.

[0051] According to the technical solution of the present invention, system debugging and optimization:

[0052] After the system installation is completed, comprehensive debugging and optimization work need to be carried out. First, calibrate the sensor module to ensure the accuracy and reliability of the collected data. Then, tune the parameters of the control algorithm, and adjust the parameters of the photovoltaic load shedding, battery voltage stabilization, and inertia damping adaptive MPC control algorithms according to the actual operating conditions to make the system achieve the best control effect. During the debugging process, simulate different grid conditions and load changes to test the response ability and stability of the system, and discover and solve problems in a timely manner. At the same time, use the communication unit to conduct joint debugging with the grid dispatching center to ensure that the system can achieve good coordinated operation with the grid.

[0053] Through the above hardware implementation, software implementation, and system debugging and optimization, the distributed energy grid-connected control device of the present invention can effectively implement the photovoltaic load shedding and battery voltage stabilization strategies and the virtual synchronous machine control strategy of inertia damping adaptive MPC, improving the stability and power quality when the distributed energy generation system is connected to the grid.

[0054] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited thereto. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, including any other suitable combination of each technical feature. These simple modifications and combinations should also be regarded as the content disclosed by the present invention and fall within the protection scope of the present invention.

Claims

1. A distributed energy grid-connected control device for a power system, characterized in that: It includes a DC side control unit and an inverter side control unit; the DC side control unit is connected to a photovoltaic array and a battery energy storage device, and adopts photovoltaic load reduction and battery voltage stabilization strategies to maintain DC bus voltage stability; the inverter side control unit is connected to an inverter, and adopts an inertia damping adaptive MPC virtual synchronous machine control strategy to achieve precise control of the inverter output power and frequency, so as to improve the stability and power quality of the power system.

2. The distributed energy grid-connected control device according to claim 1, characterized in that: The photovoltaic load reduction strategy is specifically as follows: based on real-time monitoring of environmental parameters such as light intensity and temperature and changes in grid frequency, the working point of the photovoltaic array is adjusted to deviate from the maximum power point and a certain amount of active power is reserved for backup; the adjustment process adopts an adaptive variable step maximum power point tracking (MPPT) algorithm to quickly and accurately adjust the output power of the photovoltaic array.

3. The distributed energy grid-connected control device according to claim 2, characterized in that: The adaptive variable step size MPPT algorithm comprises the following steps: (1) Collect voltage and current data of the photovoltaic array in real time and calculate the current output power; (2) Dynamically adjust the step size factor according to the grid frequency deviation and the rate of change of light intensity; (3) Based on the adjusted step factor, the reference voltage of the PV array is updated so that the operating point of the PV array moves toward the target power point.

4. The distributed energy grid-connected control device according to claim 1, characterized in that: The battery voltage stabilization strategy is specifically as follows: connecting the battery energy storage device and the DC bus through a bidirectional DC-DC converter to monitor the DC bus voltage in real time; when the DC bus voltage is higher than a set upper limit, controlling the bidirectional DC-DC converter to charge the battery energy storage device and absorb excess energy; when the DC bus voltage is lower than a set lower limit, controlling the bidirectional DC-DC converter to discharge the battery energy storage device and release energy to maintain the DC bus voltage stable; the bidirectional DC-DC converter uses a proportional-integral-differential (PID) controller for closed-loop control.

5. The distributed energy grid-connected control device according to claim 4, characterized in that: The parameters of the PID controller are adaptively adjusted in real time according to the battery state of charge (SOC), charge and discharge current, and fluctuation of the DC bus voltage to improve the accuracy and response speed of battery voltage regulation control.

6. The distributed energy grid-connected control device according to claim 1, characterized in that: The virtual synchronous machine control strategy of the inertia damping adaptive MPC includes the following steps: (1) Establish a mathematical model of the virtual synchronous machine to simulate the inertia and damping characteristics of the synchronous generator; (2) Using the model predictive control (MPC) algorithm, the system status in the future is predicted based on feedback information such as grid frequency deviation and inverter output power; (3) Based on the prediction results, the inertia coefficient and damping coefficient of the virtual synchronous machine are adjusted in real time to adapt to different grid conditions and load changes.

7. The distributed energy grid-connected control device according to claim 6, characterized in that: The objective function of the MPC algorithm comprehensively considers the frequency deviation, power deviation, and the rate of change of the inertia coefficient and the damping coefficient, and takes minimizing the objective function as the optimization goal to solve the optimal control input; the weight coefficient of the objective function is dynamically adjusted according to the operating state and stability requirements of the power grid.

8. The distributed energy grid-connected control device according to claim 6, characterized in that: The method for real-time adjustment of the inertia coefficient and damping coefficient of the virtual synchronous machine is as follows: when the grid frequency fluctuates greatly, the inertia coefficient is increased to improve the inertia response capability of the system, and the damping coefficient is increased to suppress the oscillation of the system; when the grid frequency fluctuates slightly, the inertia coefficient and damping coefficient are reduced to improve the response speed and efficiency of the system.

9. The distributed energy grid-connected control device according to claim 1, characterized in that: It also includes a communication unit, which is used to realize information interaction with the power grid dispatching center, other distributed energy power generation units and the load side; receive power grid dispatching instructions through the communication unit, obtain the operating status and load demand information of other power generation units, so as to optimize the operating strategy of the distributed energy grid-connected control device.

10. The distributed energy grid-connected control device according to claim 9, characterized in that: The communication unit adopts a combination of wireless communication technology and wired communication technology to ensure the reliability and real-time performance of communication; the wireless communication technology includes but is not limited to Wi-Fi, ZigBee, 4G / 5G, etc., and the wired communication technology includes but is not limited to Ethernet, fiber optic communication, etc.