Microgrid power station control method and device, electronic equipment and computer storage medium

Abstract

The invention provides a control method and device of a micro-grid power station, electronic equipment and a computer storage medium, is applied to a controller of the micro-grid power station, and relates to the technical field of control technologies of the micro-grid power station. The method comprises the following steps: acquiring a variable quantity of an input parameter of a micro-grid power station in a preset control period; inputting the variable quantity of the input parameter into a preset prediction model, and outputting a power grid control parameter; wherein a constraint function of the prediction model at least comprises one of output and load balance of the micro-grid power station, a condition that the voltage change of the external alternating current power grid is smaller than a preset value and total discharge amount minimization of the micro-grid power station; according to the method, the prediction model of the micro-grid power station is established, the control parameters of the micro-grid power station in the next period are predicted through the prediction model based on the variable quantity of the input parameters in the current preset control period, the target of controlling the micro-grid power station in real time is achieved through multi-target constraint, and the operation efficiency of the micro-grid power station is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of control technology for microgrid power stations, and in particular to a control method, device, electronic equipment and computer storage medium for a microgrid power station. Background Art

[0002] With the widespread application of distributed energy, microgrid power stations have become an important form of power supply. Microgrid power stations usually contain a variety of energy resources, such as solar photovoltaic, wind power generation, energy storage devices (batteries, supercapacitors, etc.) and traditional diesel generators. These different types of energy components have different dynamic characteristics and operating requirements, making it difficult for traditional control methods to effectively coordinate their work, resulting in low system operation efficiency. Microgrid power stations face a variety of uncertainties, including the intermittent nature of renewable energy (such as solar photovoltaics affected by weather, wind power generation affected by wind speed changes), dynamic changes in loads, and fluctuations in energy market prices. When faced with these uncertainties, traditional control methods often fail to adjust control strategies in a timely and accurate manner, which may lead to energy waste, reduced power supply quality, or increased economic costs. In the operation of microgrid power stations, multiple goals need to be considered simultaneously, such as power balance, voltage and frequency stability, reduced energy costs, and reduced environmental pollution. Most existing control methods can only focus on one goal, and it is difficult to achieve comprehensive optimization of multiple goals. Summary of the invention

[0003] The purpose of the present invention is to provide a control method, device, electronic device and computer storage medium for a microgrid power station. First, a prediction model of the microgrid power station is established. Based on the change in the input parameters in the current preset control cycle, the control parameters of the microgrid power station in the next cycle are predicted through the prediction model. Through multi-objective constraints, the goal of real-time control of the microgrid power station is achieved, thereby improving the operating efficiency of the microgrid power station.

[0004] In a first aspect, the present invention provides a control method for a microgrid power station, which is applied to a controller of a microgrid power station, wherein the power supply components of the microgrid power station include at least one of the following: a solar photovoltaic component, a wind power generation component, an energy storage component, and a generator component; The microgrid power station is electrically connected to the external AC power grid; Methods include: Obtaining a change in an input parameter of the microgrid power station within a preset control period; wherein the input parameter includes at least one of the following: light intensity, temperature, wind speed, and pitch angle; The change in the input parameter is input into a preset prediction model, and the grid control parameter is output; wherein the grid control parameter includes at least one of the following: the working state of the energy storage component and the output power of the generator component; the constraint function of the prediction model includes at least one of the following: the output and load balance of the microgrid power station, the minimization of the energy cost of the microgrid power station, the voltage change of the external AC power grid is less than a preset value, the frequency change of the external AC power grid is less than a preset value, and the total emission of the microgrid power station is minimized.

[0005] In some preferred embodiments of the present invention, the prediction model includes: a model of a solar photovoltaic module; The model of the solar photovoltaic module is constrained by the following formula: ; ; in, is the actual output power of the solar photovoltaic module model, The ideal output power of the solar photovoltaic module model, is the power temperature coefficient, is the actual operating temperature of the solar photovoltaic module model, Standard test temperature for solar photovoltaic module models, Standard test conditions (light intensity is , the rated power of the solar PV module model at a temperature of 25°C, is the actual light intensity, Standard test light intensity for the solar PV module model.

[0006] In some preferred embodiments of the present invention, the prediction model includes: a model of a wind power generation component; the wind power generation component includes: a wind turbine; When the wind speed is less than the cut-in wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ; When the wind speed is greater than the cut-in wind speed of the wind turbine and less than the rated wind speed of the wind turbine, the model of the wind turbine component is constrained by the following formula: = (λ, β) ; When the wind speed is greater than the rated wind speed of the wind turbine and less than the cut-out wind speed of the wind turbine, the model of the wind turbine component is constrained by the following formula: ; When the wind speed is greater than the cut-out wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ; in, is the output power of the fan, (λ, β) is the power coefficient of the wind turbine, λ is the tip speed ratio, β is the pitch angle, is the wind speed; is the rated power of the fan, ρ is the air density, and S is the area swept by the fan blades.

[0007] In some preferred embodiments of the present invention, the prediction model includes: a model of an energy storage component; The model of the energy storage component is constrained by the following formula: ; When the energy storage component is charged, the rate of change of the state of charge of the energy storage component model is constrained by the following formula: ; When the energy storage component is discharged, the rate of change of the state of charge of the energy storage component model is constrained by the following formula: ; in, is the remaining power of the energy storage component at time t, is the charge state of the energy storage component at time t, is the rated capacity of the energy storage component, is the rate of change of the state of charge of the energy storage component, is the charging current of the energy storage component at time t, is the charging efficiency of the energy storage component, is the discharge current of the energy storage component at time t, is the discharge efficiency of the energy storage component.

[0008] In some preferred embodiments of the present invention, the prediction model includes: a model of the generator component; The model of the generator assembly is constrained by the following formula: ; ; in, is the output power of the generator assembly, is the fuel consumption rate of the generator assembly, is the nth fitting coefficient, for nth component, is the efficiency of the generator assembly, It is the lower heating value of the generator components.

[0009] In some preferred embodiments of the present invention, the objective function of minimizing the energy cost of the microgrid power station is constrained by the following formula: ; Among them, J1 is the objective function of minimizing the energy cost of the microgrid power station, N is the number of control cycles in the prediction time domain, To control the cycle, is the fuel cost per unit power of the generator assembly, is the output power of the generator component in the future kth control cycle, is the unit cost of purchasing electricity from the external AC grid, is the power purchased from or sold to the external AC grid in the kth control cycle in the future.

[0010] In some preferred embodiments of the present invention, the prediction model is constrained by the following formula: ; Among them, x(k+1) is the state of the prediction model in the k+1th control cycle, A is the state transfer matrix of the prediction model, B is the input matrix of the prediction model, u(k) is the input vector of the prediction model, and w(k) is the noise vector.

[0011] In a second aspect, the present invention provides a control device for a microgrid power station, which is applied to a controller of a microgrid power station, wherein the power supply components of the microgrid power station include at least one of the following: a solar photovoltaic component, a wind power generation component, an energy storage component, and a generator component; the microgrid power station is electrically connected to an external AC power grid; The device includes: An input parameter determination module is used to obtain changes in input parameters of the microgrid power station within a preset control period; wherein the input parameters include at least one of the following: light intensity, temperature, wind speed and pitch angle; A grid control parameter determination module is used to input the change of the input parameter into a preset prediction model and output the grid control parameter; wherein the grid control parameter includes at least one of the following: the working state of the energy storage component and the output power of the generator component; the constraint function of the prediction model includes at least one of the following: the output and load balance of the microgrid power station, the minimization of the energy cost of the microgrid power station, the voltage change of the external AC power grid is less than a preset value, the frequency change of the external AC power grid is less than a preset value and the total emission of the microgrid power station is minimized.

[0012] In a third aspect, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the control method of the microgrid power station provided in the first aspect above.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the control method of the microgrid power station provided in the first aspect above.

[0014] The present invention brings the following beneficial effects: The present invention provides a control method, device, electronic device and computer storage medium of a microgrid power station, which are applied to a controller of a microgrid power station, wherein the power supply components of the microgrid power station include at least one of the following: a solar photovoltaic component, a wind power generation component, an energy storage component and a generator component; the microgrid power station is electrically connected to an external AC power grid; the method comprises: obtaining the change amount of the input parameter of the microgrid power station within a preset control cycle; wherein the input parameter includes at least one of the following: light intensity, temperature, wind speed and pitch angle; inputting the change amount of the input parameter into a preset prediction model, and outputting the grid control parameter; wherein the grid control parameter includes at least one of the following: the working state of the energy storage component and the output power of the generator component; the constraint function of the prediction model includes at least one of the following: output and load balance of the microgrid power station, minimization of the energy cost of the microgrid power station, voltage change of the external AC power grid is less than a preset value, frequency change of the external AC power grid is less than a preset value and the total emission of the microgrid power station is minimized; firstly, a prediction model of the microgrid power station is established, and the control parameters of the microgrid power station in the next cycle are predicted by the prediction model based on the change amount of the input parameter in the current preset control cycle, and the goal of real-time control of the microgrid power station is achieved through multi-objective constraints, thereby improving the operation efficiency of the microgrid power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a control method for a microgrid power station provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of an overall dynamic model of a microgrid power station provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a control device for a microgrid power station provided by an embodiment of the present invention; Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0017] Icons: 310 - input parameter determination module; 320 - power grid control parameter determination module; 400 - memory; 401 - processor; 402 - bus; 403 - communication interface. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0021] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0022] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0023] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0025] Embodiment 1 An embodiment of the present invention provides a control method for a microgrid power station, which is applied to a controller of the microgrid power station. The power supply components of the microgrid power station include at least one of the following: a solar photovoltaic component, a wind power generation component, an energy storage component and a generator component; the microgrid power station is electrically connected to an external AC power grid.

[0026] See also Figure 1 The embodiment of the present invention provides a flow chart of a control method for a microgrid power station, the method comprising: Step S102, obtaining the change of the input parameters of the microgrid power station within a preset control period; wherein the input parameters include at least one of the following: light intensity, temperature, wind speed and pitch angle.

[0027] Specifically, in each control cycle, it is first necessary to obtain the changes in various input parameters of the microgrid power station. These input parameters include at least one of the following: Light intensity: For solar photovoltaic systems, light intensity is a key factor affecting their output power. By using sensors to monitor changes in light intensity in real time, the output power of the photovoltaic system can be predicted. Temperature: Temperature has a significant impact on the output power of photovoltaic cells. Therefore, temperature changes are also important parameters that need to be obtained. Wind speed: For wind power generation systems, changes in wind speed directly affect the output power of wind turbines. By using an anemometer to monitor wind speed in real time, the output power of wind power generation systems can be predicted. Pitch angle: For wind power generation systems, changes in pitch angle will affect the power output of wind turbines. Therefore, changes in pitch angle also need to be obtained and taken into consideration.

[0028] Step S104, inputting the change of the input parameter into a preset prediction model, and outputting the grid control parameter; wherein the grid control parameter includes at least one of the following: the working state of the energy storage component and the output power of the generator component; the constraint function of the prediction model includes at least one of the following: the output and load balance of the microgrid power station, the minimization of the energy cost of the microgrid power station, the voltage change of the external AC power grid is less than a preset value, the frequency change of the external AC power grid is less than a preset value, and the total emission of the microgrid power station is minimized.

[0029] Specifically, each energy component in the microgrid power station is accurately modeled. For solar photovoltaic systems, a photovoltaic cell output power model is established based on environmental factors such as light intensity and temperature; for wind power generation systems, a power output model that takes into account wind speed, wind direction, and wind turbine characteristics is established; for energy storage devices, a model that includes battery charging and discharging characteristics, state of charge (SOC), and other factors is established; for traditional energy equipment such as diesel generators, a relationship model between their fuel consumption, output power, and efficiency is established. An overall dynamic model of the microgrid power station is established, including a voltage and current relationship model for the grid connection part, taking into account factors such as line impedance and transformer characteristics, in order to accurately describe the flow and interaction of electrical energy in the microgrid power station.

[0030] For example, for a solar photovoltaic system, the parameters of the photovoltaic cell, such as the relationship between the open circuit voltage, short circuit current, fill factor, etc. and the light intensity and temperature, are determined by combining experimental testing with theoretical analysis to establish an output power model of the photovoltaic cell. For example, a classic single diode model is used, and the model parameters are calibrated according to the characteristics of an actual photovoltaic cell.

[0031] For wind power generation systems, anemometers, wind vanes and other sensors are installed at the wind turbine site to collect a large amount of data on wind speed, wind direction and wind turbine output power. Based on these data, a wind power generation model is established using aerodynamic principles and the power curve characteristics of the wind turbine. Methods such as blade element momentum theory can be used to improve the accuracy of the model.

[0032] For energy storage devices, according to the type of battery or supercapacitor, the charge and discharge characteristic curve is obtained through charge and discharge experiments, and an energy storage model is established that takes into account factors such as SOC, charge and discharge current and voltage. For lithium batteries, an equivalent circuit model can be used to accurately describe their dynamic characteristics.

[0033] For traditional energy equipment such as diesel generators, refer to the technical parameter manual provided by the equipment manufacturer and combine it with actual operation tests to establish a relationship model between its fuel consumption and output power, while taking into account the efficiency characteristics and start-stop characteristics of the generator.

[0034] On the basis of establishing the models of each energy component, according to the electrical topology of the microgrid power station, taking into account factors such as line impedance and transformer parameters, the overall dynamic model of the microgrid power station is established using Kirchhoff's law. Through a detailed circuit analysis of the grid connection part, the voltage and current relationship equations are determined, and the models of each energy component are organically combined with the grid model.

[0035] The input parameter changes obtained above are input into the pre-set prediction model to output the grid control parameters. Specifically as follows: Working state of the energy storage component: According to the predicted light intensity and temperature changes, adjust the charging and discharging strategy of the energy storage device to ensure that the energy storage device can operate efficiently under different conditions. For example, when the light intensity is predicted to be high, increase the charging amount; when the light intensity is predicted to be low or the temperature is high, reduce the discharge amount. Output power of the generator assembly: According to the changes in wind speed and pitch angle, adjust the output power of the generator assembly. By optimizing the working state of the generator assembly, ensure that it can operate stably under various working conditions. For example, when the wind speed is high, increase the power generation; when the wind speed is low or the pitch angle is improperly adjusted, resulting in a decrease in output power, reduce the power generation.

[0036] The constraint functions of the prediction model include at least the following: Balance between the output and load of the microgrid power station: Ensure that the total power generation of the microgrid power station can meet the load demand. By real-time monitoring of load changes, adjust the working status of each energy component to ensure the stability of power supply. Minimize the energy cost of the microgrid power station: Consider the operating costs of each energy component (such as the fuel cost of the diesel generator, the depreciation and maintenance cost of the energy storage device, etc.), as well as the energy market price information, optimize the use of energy and reduce the total cost. The voltage change of the external AC power grid is less than the preset value: By controlling the working status of the generator components and the energy storage device, the grid voltage is maintained within a safe range. The frequency change of the external AC power grid is less than the preset value: Similarly, by optimizing the control strategy, the grid frequency is kept stable. Minimize the total emissions of the microgrid power station: Encourage the use of clean energy and reduce the use of traditional energy, thereby reducing pollutant emissions.

[0037] In summary, the microgrid power station control method achieves effective coordinated control of multiple energy components by accurately acquiring and analyzing the changes in input parameters, combined with advanced prediction models and multi-objective optimization algorithms. This not only improves the operating efficiency and economy of the system, but also enhances the ability to cope with uncertainty, ensuring the stable operation of the microgrid power station in a complex environment.

[0038] The present invention provides a control method for a microgrid power station, which is applied to a controller of the microgrid power station, wherein the power supply components of the microgrid power station include at least one of the following: a solar photovoltaic component, a wind power generation component, an energy storage component and a generator component; the microgrid power station is electrically connected to an external AC power grid; the method comprises: obtaining the change amount of the input parameters of the microgrid power station within a preset control cycle; wherein the input parameters include at least one of the following: light intensity, temperature, wind speed and pitch angle; inputting the change amount of the input parameters into a preset prediction model, and outputting the grid control parameters; wherein the grid control parameters include at least one of the following: the working state of the energy storage component and the output power of the generator component; the constraint function of the prediction model includes at least one of the following: output and load balance of the microgrid power station, minimization of the energy cost of the microgrid power station, voltage change of the external AC power grid is less than a preset value, frequency change of the external AC power grid is less than a preset value and the total emission of the microgrid power station is minimized; firstly, a prediction model of the microgrid power station is established, and the control parameters of the microgrid power station in the next cycle are predicted by the prediction model based on the change amount of the input parameters within the current preset control cycle, and the goal of real-time control of the microgrid power station is achieved through multi-objective constraints, thereby improving the operation efficiency of the microgrid power station.

[0039] Embodiment 2 On the basis of the above embodiments, the embodiments of the present invention provide another control method for a microgrid power station. In some preferred embodiments of the present invention, the prediction model includes: a model of a solar photovoltaic module; the model of the solar photovoltaic module is constrained by the following formula: ; ;in, is the actual output power of the solar photovoltaic module model, The ideal output power of the solar photovoltaic module model, is the power temperature coefficient, is the actual operating temperature of the solar photovoltaic module model, Standard test temperature for solar photovoltaic module models, The rated power of a solar photovoltaic module model under standard test conditions, usually referring to the light intensity ,temperature The rated power of the photovoltaic cell under is the actual light intensity, Standard test light intensity for the solar PV module model.

[0040] Specifically, the output power of photovoltaic cells is significantly affected by environmental factors such as light intensity and temperature. Its output power is basically proportional to light intensity, but an increase in temperature will cause the open circuit voltage of the cell to drop, thereby reducing the output power.

[0041] Furthermore, the power output of a wind turbine depends on wind speed, wind direction and the characteristics of the wind turbine itself, such as the wind turbine's power curve, cut-in wind speed, cut-out wind speed, etc. The wind turbine will generate electricity normally only when the wind speed is between the cut-in wind speed and the cut-out wind speed, and the output power of the wind turbine has different relationships with the wind speed in different wind speed ranges.

[0042] In some preferred embodiments of the present invention, the prediction model includes: a model of a wind power generation component; the wind power generation component includes: a wind turbine; when the wind speed is less than the cut-in wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ; When the wind speed is greater than the cut-in wind speed of the wind turbine and less than the rated wind speed of the wind turbine, the model of the wind turbine component is constrained by the following formula: = (λ, β) ; When the wind speed is greater than the rated wind speed of the wind turbine and less than the cut-out wind speed of the wind turbine, the model of the wind turbine component is constrained by the following formula: ; When the wind speed is greater than the cut-out wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ; in, is the output power of the fan, (λ, β) is the power coefficient of the fan, λ is the tip speed ratio, and And the wind speed v and fan speed It can be expressed as (where R is the radius of the wind turbine blade), β is the pitch angle, is the wind speed; is the rated power of the fan, ρ is the air density, and S is the area swept by the fan blades.

[0043] Furthermore, the performance of energy storage devices is mainly determined by the charge and discharge characteristics of the battery and the state of charge (SOC). SOC reflects the ratio of the current remaining power of the battery to the rated capacity of the battery. During the charge and discharge process, SOC will continue to change, and the charge and discharge efficiency of the battery will also change with factors such as SOC and charge and discharge current.

[0044] In some preferred embodiments of the present invention, the prediction model includes: a model of the energy storage component; the model of the energy storage component is constrained by the following formula: ; When the energy storage component is charged, the rate of change of the state of charge of the energy storage component model is constrained by the following formula: ; When the energy storage component is discharged, the rate of change of the state of charge of the energy storage component model is constrained by the following formula: ; in, is the remaining power of the energy storage component at time t, is the charge state of the energy storage component at time t, is the rated capacity of the energy storage component, is the rate of change of the state of charge of the energy storage component, is the charging current of the energy storage component at time t, is the charging efficiency of the energy storage component, is the discharge current of the energy storage component at time t, is the discharge efficiency of the energy storage component.

[0045] Further, in some preferred embodiments of the present invention, the generator assembly can be a diesel generator. There is a close relationship between the fuel consumption, output power and efficiency of a diesel generator. As the output power changes, the fuel consumption rate will change accordingly, and the efficiency of the generator under different working conditions is also different.

[0046] In some preferred embodiments of the present invention, the prediction model includes: a model of the generator assembly; the model of the generator assembly is constrained by the following formula: ; ; in, is the output power of the generator assembly, is the fuel consumption rate of the generator assembly, is the nth fitting coefficient, for nth component, is the efficiency of the generator assembly, It is the lower heating value of the generator components.

[0047] Furthermore, the overall dynamic model of the microgrid power station is established, considering the line impedance and transformer characteristics. For the line connecting the microgrid power station and the external power grid, the line resistance is set to , the inductance is , the transformer ratio is k, and the primary voltage is , the secondary voltage is , the primary current is , the secondary current is .

[0048] According to the transformer principle, there is and .

[0049] For the line part, according to Ohm's law and Kirchhoff's law, in the case of AC, the relationship between voltage and current can be expressed as: .

[0050] Will Substituting into the above formula, we get: .

[0051] Furthermore, in a microgrid power station, electric energy flows and interacts between various energy components. For example, the electric energy generated by the solar photovoltaic system and the wind power generation system may be directly supplied to the load, or may charge the energy storage device; the energy storage device can discharge to the load or feed back electric energy to the grid when needed; the diesel generator starts to generate electricity when other energy sources are insufficient, and the electric energy it generates will also participate in the power distribution of the entire microgrid power station.

[0052] In order to accurately describe the flow and interaction of this electric energy, it is necessary to establish the power balance equation between each energy component, load, and power grid. Assume that the output power of the solar photovoltaic system is , the output power of the wind power generation system is , the output power of the energy storage device is , the output power of the diesel generator is , the power consumed by the load is , the power exchanged with the external grid is , then: .

[0053] At the same time, it is necessary to consider the dynamic characteristics of each energy component, such as the change of photovoltaic cell output power with light intensity and temperature, the change of wind turbine output power with wind speed and wind direction, the change of energy storage device SOC, and the change of diesel generator fuel consumption and efficiency, etc. Substituting these dynamic characteristics into the above power balance equation, a complete overall dynamic model of the microgrid power station can be established. Figure 2 The illustrated embodiment of the present invention provides a schematic structural diagram of an overall dynamic model of a microgrid power station.

[0054] Furthermore, based on the system model established above, an MPC prediction model is constructed. This prediction model can predict the state changes of the microgrid power station in multiple control cycles in the future based on the current system state (including the output power of each energy component, the SOC of the energy storage device, the load power, etc.) and the prediction information in the future (such as the light intensity and wind speed prediction in the weather forecast, load prediction, etc.). The prediction model adopts the form of discrete time state space equations, which can accurately reflect the dynamic characteristics of the system and has sufficient accuracy to support effective control decisions.

[0055] The objective function of MPC is set by comprehensively considering multiple control objectives of the microgrid power station. The objective function includes: power balance item, which ensures that the total power generation power of the microgrid power station and the sum of the load power and the charging and discharging power of the energy storage device are balanced at every moment; voltage and frequency stability item, which ensures the quality of power by penalizing the degree to which the voltage and frequency deviate from the rated value; energy cost item, which optimizes the use of energy and reduces the total cost according to the operating costs of different energy components (such as the fuel cost of diesel generators, the depreciation and maintenance cost of energy storage devices, etc.) and energy market price information; environmental protection index item, which considers the pollutant emissions in the power generation process of different energy sources, encourages the use of clean energy, and reduces environmental pollution.

[0056] Microgrid power stations contain multiple energy components, and the costs of different energy sources are different. For example, the fuel cost of diesel generators is relatively high, while the costs of solar photovoltaic and wind power generation are mainly in the equipment investment and maintenance stages, and the energy costs during operation are lower (when environmental costs are not considered).

[0057] In some preferred embodiments of the present invention, the objective function of minimizing the energy cost of the microgrid power station is constrained by the following formula: ; Where J1 is the objective function of minimizing the energy cost of the microgrid power station, N is the number of control cycles in the prediction time domain, To control the cycle, is the fuel cost per unit power of the generator assembly, is the output power of the generator component in the future kth control cycle, is the unit cost of purchasing electricity from the external AC grid, is the power purchased from or sold to the external AC grid in the kth control cycle in the future. Appropriate weight coefficients are assigned to each item in the objective function to adjust the priority of different objectives according to actual needs.

[0058] Consider various physical constraints in the operation of microgrid power stations. For each energy component, there are power output restrictions, such as maximum power tracking restrictions for solar photovoltaic and wind power generation, charging and discharging power restrictions for energy storage devices, and upper and lower limits of SOC; for the grid connection part, there are voltage and current amplitude restrictions, power factor restrictions, etc. In the design of the MPC controller, these constraints are used as constraints for the optimization problem to ensure that the generated control strategy achieves the optimization of the objective function under the premise of meeting the safe and stable operation of the system.

[0059] Furthermore, MPC is an advanced model-based control strategy that obtains the control action at the current moment by solving a finite-time open-loop optimal control problem at each sampling moment. This finite time domain is determined based on a prediction model, which takes into account the future dynamic behavior of the system. In the control application of the microgrid power station provided in the embodiment of this aspect, MPC uses the current state and prediction information of the system to find a set of control input sequences through an optimization algorithm in each control cycle so that the objective function reaches the optimal value in the prediction time domain while satisfying the constraints. Then, only the first control input of this optimal control sequence is applied to the actual system, and this process is repeated in the next control cycle to form a rolling time domain optimization control.

[0060] For a complex system like a microgrid power station, each energy component it contains (such as solar photovoltaic systems, wind power systems, energy storage devices, and diesel generators, etc.) has its own dynamic response time. For example, the output power of a solar photovoltaic system responds relatively quickly to changes in light intensity, while the charging and discharging process and state changes of the energy storage device (such as changes in the state of charge SOC) may be relatively slow. The control cycle needs to be shorter than the fastest dynamic response time in the system to ensure that changes in the system state can be captured and responded to in a timely manner. If the control cycle is too long, the effective control opportunity for rapidly changing components may be missed. For example, for a photovoltaic system with a response time in seconds, the control cycle may need to be set to less than 1 second to accurately control its output power.

[0061] MPC (Model Predictive Control) relies on forecast information to optimize the control input sequence. The forecast information includes future light intensity, wind speed, load demand, etc. The accuracy of these forecast information decreases as the forecast time domain increases. The control period should be determined based on the effective time range of the forecast information. If the forecast light intensity is valid for 10 minutes and the forecast accuracy is high in the first 2 minutes, then the control period can be considered to be set to less than 2 minutes, so that the high-precision forecast information can be fully utilized for optimized control.

[0062] The optimization algorithm in MPC requires a certain amount of computing resources to solve the optimal control input sequence. If the control cycle is too short, complex calculations need to be performed in each cycle, which may lead to insufficient computing resources or too long calculation time, and cannot meet the requirements of real-time control. Complex microgrid power station system models and optimization objective functions will increase the complexity of the algorithm. For example, when considering many factors such as detailed dynamic models of multiple energy components, line losses, and charging and discharging efficiency of energy storage devices, the amount of calculation of the optimization algorithm will increase significantly. Therefore, the control cycle needs to be reasonably set according to the processing power of the computing device while ensuring system performance, so as to avoid system delays or crashes caused by excessively frequent calculations.

[0063] Different control objectives have different requirements for the control cycle. If the control goal is to accurately track rapid changes in load demand, a shorter control cycle is required to quickly adjust the output power of the energy component. For example, in load scenarios such as data centers that have extremely high requirements for power quality, the control cycle may need to be set to seconds or even shorter to ensure the stability of power supply. The stability of the system is also an important factor in determining the control cycle. Too frequent or unreasonable control inputs may cause system oscillation or instability. For example, for the charge and discharge control of energy storage devices, if the control cycle is too short, frequent charge and discharge instructions may reduce battery life or even cause system failure. Therefore, it is necessary to determine a suitable control cycle through stability analysis so that the system can achieve a balance between effective control and stable operation.

[0064] The prediction model is the core part of MPC. In the embodiment of the present invention, based on the previously established mathematical models of the components and the whole of the microgrid power station, it is discretized to obtain a prediction model in the form of a discrete time state space equation. The state equation describes how the system state evolves over time, and it is associated with the input (i.e., control variables) and output of the system. For example, for a simple microgrid subsystem including solar photovoltaics, energy storage and load, the state variables may include the output power of photovoltaics, the SOC of energy storage, etc., the input variables are the charging and discharging power instructions for energy storage, etc., and the output variables may be the voltage and power at the grid connection point. The rolling optimization mechanism enables MPC to adapt to the dynamic changes of the system. In each control cycle, MPC updates the current state of the system according to the latest measurement value, and then predicts the future state based on the prediction model. It searches for the control sequence that optimizes the objective function within the prediction time domain (e.g., the next 10 sampling cycles). However, only the first control action of this sequence is implemented, and then it is re-optimized in the next cycle. In this way, even when there is uncertainty in the system (such as fluctuations in renewable energy power and changes in load), the control strategy can be continuously adjusted.

[0065] The objective function determines the optimization direction of the control strategy in MPC. In this microgrid power station control method, the objective function integrates multiple objectives. Taking power balance as an example, the power balance term in the objective function can be expressed as the sum of the squares of the difference between the generated power and the load power and the energy storage charging and discharging power. For the voltage and frequency stability targets, the weighted square sum of the deviations of voltage and frequency from the rated value can be used. The energy cost term is calculated according to the cost function of different energy components, such as the fuel cost of diesel generators is related to the operating time, and the energy storage cost is related to the number of charging and discharging times and SOC changes. The environmental protection index item can be calculated based on the pollutant emission coefficient and usage of different energy sources. By adjusting the weight coefficients of each item, the balance and priority setting between different objectives can be achieved. Constraints are crucial in the MPC algorithm. For the power output limit of energy components, such as the maximum power point tracking (MPPT) limit of solar photovoltaics, the upper limit is determined based on the characteristics of photovoltaic cells and the light temperature conditions. The charge and discharge power limit of the energy storage device is determined according to the battery type and SOC, and the SOC has upper and lower limit constraints (such as lithium batteries are generally 0.2-0.8). For the grid connection part, the voltage amplitude limit is based on the grid standard (for example, the voltage amplitude of the low-voltage distribution network is usually within ±10% of the rated voltage), the current amplitude limit takes into account the line capacity and the rated current of the equipment, and the power factor limit ensures the quality of power (generally required to be no less than 0.9). During the optimization process, the MPC algorithm converts these constraints into constraints of the optimization problem to ensure that the control strategy found is feasible and does not cause the system to violate physical limitations.

[0066] At the beginning of each control cycle, the MPC controller obtains the system measurement values ​​at the current moment (measured by sensors, the actual output power of each energy component, the SOC of the energy storage device, the load power, voltage, frequency and other parameters), and combines the prediction model and future prediction information to perform online optimization and solution of the objective function. Use efficient optimization algorithms (such as interior point method, sequential quadratic programming, etc.) to solve the optimization problem and obtain the optimal control sequence of each energy component in the next control cycle, including the power reference value of solar photovoltaic and wind power generation, the charging and discharging power instructions of the energy storage device, and the output power setting value of traditional energy equipment such as diesel generators. The first value in the control sequence is used as the actual control instruction of the current control cycle and sent to the corresponding energy component actuator to realize real-time control of the microgrid power station. Then, repeat the above process in the next control cycle to realize rolling optimization control.

[0067] Discretize the energy component models and the overall model of the microgrid power station established above and convert them into a prediction model in the form of discrete time state space equations. Determine the state variables, input variables, and output variables of the model. For example, state variables may include the output power of each energy component, the SOC of the energy storage device, etc.; input variables are control instructions (such as the power reference value of each energy component); output variables are measurable system parameters (such as voltage, current, frequency, etc.). Determine the prediction time domain and control time domain based on the dynamic characteristics and control cycle requirements of the actual system. The prediction time domain should be long enough to cover the main dynamic processes of the system, and the control time domain should be reasonably selected based on the complexity of the control algorithm and the response speed of the actuator.

[0068] In some preferred embodiments of the present invention, the prediction model is constrained by the following formula: ; Wherein, x(k+1) is the state of the prediction model in the k+1th control cycle, A is the state transfer matrix of the prediction model, B is the input matrix of the prediction model, u(k) is the input vector of the prediction model, and w(k) is the noise vector.

[0069] First, define the system state vector , which contains the key state information of the microgrid power station at discrete time step k (corresponding to each control cycle): ; in, is the output power of the solar photovoltaic system in the kth control cycle, is the output power of the wind power generation system in the kth control cycle, SOC(k) is the state of charge of the energy storage device in the kth control cycle, is the output power of the diesel generator in the kth control cycle, is the power consumed by the load in the kth control cycle.

[0070] Define the system input vector u(k), which contains the input variables that can be used to control the operation of the microgrid power station: ; in, represents the control input to the solar photovoltaic system (for example, it may involve operations such as adjusting the angle of the photovoltaic panel, and here a variable is simply used to represent the relevant control means), Represents the control input to the wind power generation system (such as adjusting the pitch angle of the wind turbine, etc.), Indicates the control input to the energy storage device (such as the instruction to control charging and discharging), Represents the control input to the diesel generator (such as adjusting the fuel supply, etc.).

[0071] According to the dynamic characteristics of each component of the microgrid power station and their mutual relationship, the state equation is constructed as follows: ; Among them, A is the system state transfer matrix, which describes the natural evolution of the system from the current state x(k) to the next state x(k+1) when there is no external input (i.e. u(k)=0). The values ​​of its specific elements depend on the dynamic models of each energy component and the coupling relationship between them. For example, for a solar photovoltaic system, the change in its output power may be related to factors such as light intensity, temperature, and its own attenuation. The influence of these factors in discrete time will be reflected in the corresponding elements of the A matrix; for energy storage devices, the change in the state of charge is related to the charging and discharging current, battery characteristics, etc., which will also be reflected in the A matrix. B is the input matrix, which represents the degree of influence of the system input u(k) on the system state x(k+1). Different control inputs have different effects on each state variable. For example, the control input of the solar photovoltaic system Mainly affects its output power , which will be reflected in the corresponding elements of the B matrix. w(k) is the process noise vector, which takes into account various uncertainties in the actual system, such as random fluctuations in environmental factors (measurement errors or unpredictable small changes in light intensity, wind speed, etc.), small changes in equipment performance, etc. These uncertainties will cause the actual state of the system to deviate from the state predicted by the model, and this deviation is modeled by introducing w(k).

[0072] Construct the output equation to describe the observable output of the system. Here, the output vector y(k) is defined as: ; Where C is the output matrix, which determines which elements in the system state vector x(k) can be directly observed and how to combine them into the output vector y(k). For example, you may only be interested in the sum of the output power of certain energy components and the difference in load power as observable outputs, then the C matrix will be set according to this requirement. v(k) is the measurement noise vector, which takes into account the errors in measuring the system state and output, such as the accuracy limitations of the sensor. Due to the existence of measurement errors, the actual observed output y(k) will deviate from the output calculated based on the theoretical model. This deviation is modeled by introducing v(k).

[0073] Determine the relevant matrix elements based on the solar photovoltaic system model: Determine the relevant matrix elements based on the solar photovoltaic system model: According to the output power model of the solar photovoltaic system established previously, such as: ;in, It is the change in the output power of the photovoltaic system from the kth control cycle to the k+1th control cycle, which depends on factors such as changes in light intensity and temperature. By analyzing the influence of these factors on the output power, the elements related to the solar photovoltaic system in the A matrix and the corresponding control input in the B matrix can be determined. influencing elements.

[0074] Determine the relevant matrix elements based on the wind power generation system model: For a wind power generation system, according to its power output model, for example, when the wind speed is within a certain range: ;in, It is the change in the output power of the wind power generation system from the kth control cycle to the k+1th control cycle, which depends on factors such as wind speed change and pitch angle change. By analyzing the influence of these factors on the output power, we can determine the elements related to the wind power generation system in the A matrix and the corresponding control inputs in the B matrix to the control inputs of the wind power generation system. influencing elements.

[0075] Determine the relevant matrix elements based on the energy storage device model: According to the charge state change model of the energy storage device, such as during the charging process: ; During the discharge process: ;in, is the charging current, is the charging efficiency, is the rated capacity of the battery, is the discharge current, By analyzing these equations and the relationship between the charge and discharge operations and other state variables, the elements related to the energy storage device in the A matrix and the corresponding control inputs in the B matrix can be determined. influencing elements.

[0076] Determine relevant industry elements based on diesel generator models: Based on diesel generator fuel consumption, output power and efficiency models, for example: ;in, It is the change in the output power of the diesel generator from the kth control cycle to the k+1th control cycle, which depends on factors such as changes in fuel consumption and engine efficiency. By analyzing the influence of these factors on the output power, the elements related to the diesel generator in the A matrix and the corresponding control input in the B matrix can be determined. influencing elements.

[0077] Predicting future state changes, given the current state vector x(0) and the predicted input vector u(k) (where (k=0, 1, ..., N-1), N is the number of control cycles in the prediction time domain), we can use the discrete time state space equation to predict the future state changes of the microgrid power station. When (k=0): ; When (k=1): ; When (k=n): ; Therefore, we can predict the state vector x(k) (where (k=1, 2, ..., N)) to describe the future state changes of the microgrid power station in the prediction time domain. This prediction model based on discrete time state space equations can accurately reflect the dynamic characteristics of the system and provide sufficient accuracy to support effective control decisions.

[0078] According to the operation requirements of the microgrid power station and the actual economic and environmental factors, the specific expressions of each item in the objective function are determined. For the power balance item, the square sum of the difference between the generated power and the load power and the energy storage charging and discharging power can be used; for the voltage and frequency stability items, the absolute value or square sum of the voltage and frequency deviation from the rated value can be used; for the energy cost item, the total cost is calculated according to the cost function of different energy sources; for the environmental protection index item, the total emission is calculated according to the pollutant emission coefficient of different energy sources. By analyzing the importance and actual operation of different goals, appropriate weight coefficients are assigned to each item in the objective function. For example, in areas that focus on environmental protection, the weight coefficient of the environmental protection index item can be appropriately increased; in application scenarios that are sensitive to energy costs, the weight of the energy cost item is increased.

[0079] For the power output limit of each energy component, the specific upper and lower limits are determined based on the rated parameters of the equipment and actual operating experience. For example, the maximum power output of the solar photovoltaic system is affected by the light intensity and temperature, and its power upper limit is determined based on the maximum power tracking curve of the photovoltaic cell; the charging and discharging power limit of the energy storage device is determined based on the rated power and SOC status of the battery or supercapacitor, and the reasonable upper and lower limits of the SOC are set at the same time (such as the SOC range of lithium batteries is generally 0.2-0.8). For the voltage and current amplitude limits of the grid connection part, they are determined according to the local grid standards and the insulation level of the equipment. For example, for low-voltage distribution networks, the voltage amplitude is usually limited to ±10% of the rated voltage; the power factor limit is set according to the requirements of the grid and the power quality standards, and generally requires the power factor to be no less than 0.9.

[0080] Furthermore, sensors are installed at key locations of the microgrid power station, including the output terminals of each energy component, energy storage devices, grid connection points, etc. Sensor types include voltage sensors, current sensors, power sensors, temperature sensors, etc., which are used to measure the operating parameters of the system in real time. The data measured by the sensors is transmitted to the MPC controller through the data acquisition system. The data acquisition system should ensure the accuracy and real-time nature of the data, and adopt appropriate data transmission protocols (such as Modbus, CAN, etc.) and communication networks (such as wired Ethernet, wireless ZigBee, etc.).

[0081] At the beginning of each control cycle, the MPC controller receives system measurements and combines them with forecast information such as light intensity and wind speed obtained from the weather forecast system and load forecast data, and substitutes these data into the prediction model and objective function. The objective function is optimized and solved online using the selected optimization algorithm (such as the interior point method). During the solution process, the constraints are used as constraints of the optimization problem to ensure that the solved control sequence meets the physical constraints of the system. The optimization algorithm should have sufficient computing speed to ensure that the solution is completed within the control cycle.

[0082] The first value in the control sequence obtained by optimization is used as the control instruction of the current control cycle and sent to the actuators of each energy component through the communication interface. For example, for a solar photovoltaic system, the power reference value is sent to the controller of the photovoltaic inverter to adjust the working point of the photovoltaic cell; for an energy storage device, the charge and discharge power instructions are sent to the battery management system to control the charge and discharge process of the energy storage device; for a diesel generator, the output power setting value is sent to the speed regulator and excitation regulator of the generator to adjust the output power of the generator. During the execution of the control instructions, the operating status of the system is continuously monitored to ensure that each energy component executes accurately according to the control instructions. If there is an execution deviation or fault, timely adjustment or alarm processing is carried out. By continuously repeating the above online optimization and control process, the stable and efficient operation of the microgrid power station is achieved.

[0083] The embodiment of the present invention provides a control method for a microgrid power station. Through accurate system modeling and the design of an MPC controller, it can effectively coordinate the work of various types of energy components in the microgrid power station, realize reasonable complementarity between them, improve the operating efficiency of the system, make full use of renewable energy, and reduce dependence on traditional energy; using the prediction model and online optimization mechanism, the MPC controller can timely take into account the intermittent nature of renewable energy, load changes and other uncertain factors. According to the latest prediction information and actual measurement values, the control strategy is adjusted to ensure the stable operation of the microgrid power station in a complex and changeable environment, and reduce energy waste and power supply quality problems caused by uncertainty; the objective function in the MPC control method comprehensively considers multiple objectives such as power balance, voltage and frequency stability, energy cost and environmental protection, and flexibly adjusts the objective priority through weight coefficients. This multi-objective optimization capability can reduce operating costs, improve energy utilization efficiency and reduce environmental pollution while ensuring stable power supply of the microgrid power station, achieving a win-win situation of economic and environmental benefits.

[0084] Embodiment 3 Based on the above embodiments, an embodiment of the present invention provides a control device for a microgrid power station, which is applied to a controller of a microgrid power station. The power supply components of the microgrid power station include at least one of the following: solar photovoltaic components, wind power generation components, energy storage components and generator components; the microgrid power station is electrically connected to an external AC power grid.

[0085] See also Figure 3 The structure diagram of a control device of a microgrid power station provided by an embodiment of the present invention is shown, and the device includes: The input parameter determination module 310 is used to obtain the change of the input parameters of the microgrid power station within a preset control period; wherein the input parameters include at least one of the following: light intensity, temperature, wind speed and pitch angle.

[0086] The grid control parameter determination module 320 is used to input the change of the input parameter into a preset prediction model and output the grid control parameter; wherein the grid control parameter includes at least one of the following: the working state of the energy storage component and the output power of the generator component; the constraint function of the prediction model includes at least one of the following: the output and load balance of the microgrid power station, the minimization of the energy cost of the microgrid power station, the voltage change of the external AC power grid is less than the preset value, the frequency change of the external AC power grid is less than the preset value and the total emission of the microgrid power station is minimized.

[0087] Furthermore, in some preferred embodiments of the present invention, the prediction model includes: a model of a solar photovoltaic module; the model of a solar photovoltaic module is constrained by the following formula: ; ;in, is the actual output power of the solar photovoltaic module model, The ideal output power of the solar photovoltaic module model, is the power temperature coefficient, is the actual operating temperature of the solar photovoltaic module model, Standard test temperature for solar photovoltaic module models, Standard test conditions (light intensity is , the rated power of the solar PV module model at a temperature of 25°C, is the actual light intensity, Standard test light intensity for the solar PV module model.

[0088] Further, in some preferred embodiments of the present invention, the prediction model includes: a model of a wind power generation component; the wind power generation component includes: a wind turbine; When the wind speed is less than the cut-in wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ; When the wind speed is greater than the cut-in wind speed of the wind turbine and less than the rated wind speed of the wind turbine, the model of the wind turbine component is constrained by the following formula: = (λ, β) ; When the wind speed is greater than the rated wind speed of the wind turbine and less than the cut-out wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ; When the wind speed is greater than the cut-out wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ;in, is the output power of the fan, (λ, β) is the power coefficient of the wind turbine, λ is the tip speed ratio, β is the pitch angle, is the wind speed; is the rated power of the fan, ρ is the air density, and S is the area swept by the fan blades.

[0089] Furthermore, in some preferred embodiments of the present invention, the prediction model includes: a model of the energy storage component; the model of the energy storage component is constrained by the following formula: ; When the energy storage component is charged, the rate of change of the state of charge of the energy storage component model is constrained by the following formula: ; When the energy storage component is discharged, the rate of change of the state of charge of the energy storage component model is constrained by the following formula: ;in, is the remaining power of the energy storage component at time t, is the charge state of the energy storage component at time t, is the rated capacity of the energy storage component, is the rate of change of the state of charge of the energy storage component, is the charging current of the energy storage component at time t, is the charging efficiency of the energy storage component, is the discharge current of the energy storage component at time t, is the discharge efficiency of the energy storage component.

[0090] Furthermore, in some preferred embodiments of the present invention, the prediction model includes: a model of the generator assembly; the model of the generator assembly is constrained by the following formula: ; ;in, is the output power of the generator assembly, is the fuel consumption rate of the generator assembly, is the nth fitting coefficient, for nth component, is the efficiency of the generator assembly, It is the lower heating value of the generator components.

[0091] Furthermore, in some preferred embodiments of the present invention, the objective function of minimizing the energy cost of the microgrid power station is constrained by the following formula: ; Where J1 is the objective function of minimizing the energy cost of the microgrid power station, N is the number of control cycles in the prediction time domain, To control the cycle, is the fuel cost per unit power of the generator assembly, is the output power of the generator component in the future kth control cycle, is the unit cost of purchasing electricity from the external AC grid, is the power purchased from or sold to the external AC grid in the kth control cycle in the future.

[0092] Furthermore, in some preferred embodiments of the present invention, the prediction model is constrained by the following formula: ; Wherein, x(k+1) is the state of the prediction model in the k+1th control cycle, A is the state transfer matrix of the prediction model, B is the input matrix of the prediction model, u(k) is the input vector of the prediction model, and w(k) is the noise vector.

[0093] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the control device of the microgrid power station described above can refer to the corresponding process in the aforementioned embodiment of the control method of the microgrid power station, and will not be repeated here.

[0094] Embodiment 4 The embodiment of the present invention also provides an electronic device for operating a control method of a microgrid power station; see Figure 4 The structural schematic diagram of an electronic device provided by an embodiment of the present invention is shown, and the electronic device includes a memory 400 and a processor 401, wherein the memory 400 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 401 to implement the control method of the above-mentioned microgrid power station.

[0095] Further, Figure 4 The electronic device shown further includes a bus 402 and a communication interface 403 , and the processor 401 , the communication interface 403 and the memory 400 are connected via the bus 402 .

[0096] The memory 400 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0097] The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 401. The above processor 401 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 400, and the processor 401 reads the information in the memory 400 and completes the steps of the method of the above embodiment in combination with its hardware.

[0098] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the control method of the above-mentioned microgrid power station. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0099] The computer program product of the control method, device and electronic device of the microgrid power station provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method in the previous method embodiment. The specific implementation can be referred to the method embodiment, which will not be repeated here.

[0100] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and / or device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0101] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0102] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for a microgrid power station, characterized in that: A controller applied to a microgrid power station, wherein the power supply components of the microgrid power station include at least one of the following: a solar photovoltaic component, a wind power generation component, an energy storage component, and a generator component; The microgrid power station is electrically connected to an external AC power grid; The method comprises: Obtaining a change in an input parameter of the microgrid power station within a preset control period; wherein the input parameter includes at least one of the following: light intensity, temperature, wind speed and pitch angle; The change in the input parameter is input into a preset prediction model, and a grid control parameter is output; wherein the grid control parameter includes at least one of the following: the working state of the energy storage component and the output power of the generator component; the constraint function of the prediction model includes at least one of the following: the output and load balance of the microgrid power station, the minimization of the energy cost of the microgrid power station, the voltage change of the external AC power grid is less than a preset value, the frequency change of the external AC power grid is less than a preset value, and the total emission of the microgrid power station is minimized.

2. The control method of the microgrid power station according to claim 1, characterized in that: The prediction model includes: a model of a solar photovoltaic module; The model of the solar photovoltaic module is constrained by the following formula: ； ； in, is the actual output power of the solar PV module model, The ideal output power of the solar PV module model is, is the power temperature coefficient, is the actual operating temperature of the solar PV module model, The standard test temperature for the solar photovoltaic module model is Standard test conditions (light intensity is , the rated power of the solar PV module model at a temperature of 25°C, is the actual light intensity, Standard test light intensity for the solar PV module model.

3. The control method of the microgrid power station according to claim 2, characterized in that: The prediction model includes: a model of a wind power generation component; the wind power generation component includes: a wind turbine; When the wind speed is less than the cut-in wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ； When the wind speed is greater than the cut-in wind speed of the wind turbine and less than the rated wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: = (l, b) ; When the wind speed is greater than the rated wind speed of the wind turbine and less than the cut-out wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ； When the wind speed is greater than the cut-out wind speed of the wind turbine, the model of the wind power generation component is constrained by the following formula: ； in, is the output power of the fan, (λ, β) is the power coefficient of the wind turbine, λ is the tip speed ratio, β is the pitch angle, is the wind speed, is the rated power of the fan, ρ is the air density, and S is the area swept by the fan blades.

4. The control method of the microgrid power station according to claim 3, characterized in that: The prediction model includes: a model of an energy storage component; The model of the energy storage component is constrained by the following formula: ； When the energy storage component is charged, the rate of change of the state of charge of the model of the energy storage component is constrained by the following formula: ； When the energy storage component is discharged, the rate of change of the state of charge of the model of the energy storage component is constrained by the following formula: ； in, is the remaining power of the energy storage component at time t, is the charge state of the energy storage component at time t, is the rated capacity of the energy storage component, is the rate of change of the state of charge of the energy storage component, is the charging current of the energy storage component at time t, is the charging efficiency of the energy storage component, is the discharge current of the energy storage component at time t, is the discharge efficiency of the energy storage component.

5. The control method of the microgrid power station according to claim 4, characterized in that: The prediction model includes: a model of a generator component; The model of the generator assembly is constrained by the following formula: ； ； in, is the output power of the generator assembly, is the fuel consumption rate of the generator assembly, is the nth fitting coefficient, for nth component, is the efficiency of the generator assembly, is the lower heating value of the generator assembly.

6. The control method of the microgrid power station according to claim 5, characterized in that: The objective function of minimizing the energy cost of the microgrid power station is constrained by the following formula: ； Wherein, J1 is the objective function of minimizing the energy cost of the microgrid power station, N is the number of control cycles in the prediction time domain, To control the cycle, is the fuel cost per unit power of the generator assembly, is the output power of the generator assembly in the future kth control cycle, is the unit cost of purchasing electricity from the external AC grid, is the power purchased from or sold to the external AC grid in the kth control cycle in the future.

7. The control method of the microgrid power station according to claim 6, characterized in that: The prediction model is constrained by the following formula: ； Among them, x(k+1) is the state of the prediction model in the k+1th control cycle, A is the state transfer matrix of the prediction model, B is the input matrix of the prediction model, u(k) is the input vector of the prediction model, and w(k) is the noise vector.

8. A control device for a microgrid power station, characterized in that: A controller applied to a microgrid power station, wherein the power supply components of the microgrid power station include at least one of the following: a solar photovoltaic component, a wind power generation component, an energy storage component and a generator component; the microgrid power station is electrically connected to an external AC power grid; The device comprises: An input parameter determination module, used to obtain changes in input parameters of the microgrid power station within a preset control period; wherein the input parameters include at least one of the following: light intensity, temperature, wind speed and pitch angle; A grid control parameter determination module is used to input the change of the input parameter into a preset prediction model and output a grid control parameter; wherein the grid control parameter includes at least one of the following: the working state of the energy storage component and the output power of the generator component; the constraint function of the prediction model includes at least one of the following: the output and load balance of the microgrid power station, the minimization of the energy cost of the microgrid power station, the voltage change of the external AC power grid is less than a preset value, the frequency change of the external AC power grid is less than a preset value and the total emission of the microgrid power station is minimized.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the control method of the microgrid power station as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the control method of the microgrid power station according to any one of claims 1 to 7.

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