Bidirectional iterative communication-based micro-grid coordination control method, system and device

Abstract

The invention provides a micro-grid coordination control method, system and device based on bidirectional iterative communication, and belongs to the technical field of new energy micro-grid control and application. The method comprises the following steps: firstly, designing respective sliding mode variable structure sub-controllers under all working conditions according to dynamic characteristics of wind and light subsystems; and then, an economic objective function is established in a supervision layer in combination with the process control performance and economic performance of the system, a distributed optimization problem is designed under corresponding control constraints, each subsystem performs iterative interaction based on a bidirectional communication channel, and then the optimization problem is solved. And finally, taking the optimized output as the reference input of each subsystem controller through a coordination layer so as to coordinate the unit output of each subsystem. According to the invention, the reliability, interactivity and economy of micro-grid coordination control are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy microgrid control and application technology, and more specifically to a microgrid coordinated control method, system and device based on bidirectional iterative communication. Background Art

[0002] As a new type of power system under energy transformation, coordinated control of microgrids can not only significantly improve the absorption rate of renewable energy, but also effectively smooth out power fluctuations caused by the randomness and intermittency of wind and solar power generation. Batteries, as key energy storage devices in microgrid power generation systems, can play a role in smoothing power fluctuations and transitional power supply. Therefore, under the inherent requirements of achieving sustainable development and responding to environmental challenges, the development of new power systems including energy storage has become an inevitable trend.

[0003] However, behind the large-scale installation of new energy, the problem of its effective absorption has become increasingly prominent. On the one hand, due to the inherent intermittent and random power generation characteristics of new energy such as wind power and photovoltaic power, the distribution of electric energy is extremely uneven in time and space. From the demand side, it often causes the risk of supply shortage. At the same time, since new energy power generation is greatly affected by changes in the external environment, the probability of system failure is greatly increased, which poses a severe challenge to the safe and stable operation of the power system. On the other hand, although the cost of new energy power generation has decreased, the cost of regulating the system in the face of risks has increased significantly. Due to the particularity of new energy power generation, it requires coordinated dispatching of multiple industries and multiple systems, which brings challenges to the economic operation of the power system. In order to meet the user's requirements for high-quality, high-reliability electric energy and the demand for diversified power supply, microgrids with distributed power sources as the main unit, as an important part of smart grids, have begun to receive more and more attention.

[0004] In general, microgrids can achieve integrated and coordinated operation of internal power sources and loads. The development and extension of microgrids can promote large-scale access to distributed power sources and renewable energy, and achieve efficient supply of multiple energy forms to loads. It is an effective way to realize active distribution networks and plays an important bridge role in the transition from traditional power grids to smart grids. However, in the actual operation of microgrids, distributed energy sources such as wind and solar are greatly affected by natural conditions and geographical factors, and have uncontrollable characteristics such as intermittent and random, which greatly increases the difficulty of controlling and managing microgrids. On the one hand, it is inevitable that there will be problems of imbalance between supply and demand, which will affect the electricity demand on the user side; on the other hand, it will cause power fluctuations in microgrid power generation, which will affect the service life of power generation equipment and reduce the economic efficiency of microgrid operation. Therefore, how to improve the security, flexibility and economy of multi-energy supply on the basis of ensuring the balance between supply and demand is an urgent problem to be solved by microgrids. Summary of the invention

[0005] In view of the problems existing in the prior art, the purpose of the present invention is to provide a microgrid coordinated control method, system and device based on bidirectional iterative communication, which can effectively improve the reliability, interactivity and economy of microgrid coordinated control.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions: A microgrid coordinated control method based on bidirectional iterative communication, comprising: Analyze the working principles of wind power generation unit, photovoltaic power generation unit and battery energy storage unit, and establish mathematical models of wind power generation subsystem, photovoltaic power generation subsystem and battery energy storage subsystem according to the dynamic characteristics of each unit; Based on the sliding mode control theory, the sliding mode variable structure controller of the wind subsystem and the sliding mode variable structure controller of the photovoltaic subsystem are designed for the wind power generation subsystem and the photovoltaic generation subsystem respectively under all working conditions; An objective function is established by combining the process control performance and economic performance of the system, and a distributed economic model prediction controller is designed at the coordination layer, wherein the distributed economic model prediction controller includes a distributed coordination controller of a wind power generation subsystem and a coordination controller of a photovoltaic power generation subsystem; Bidirectional iterative communication and information interaction are carried out through the coordination layer, and the output of the iterative optimization of the distributed economic model predictive controller is used as the reference input of the controller of each subsystem of the microgrid to coordinate the unit output of each subsystem of the microgrid accordingly.

[0007] Furthermore, the mathematical model of the wind power generation subsystem includes:

[0008] in, is the state vector of the wind electronic system; and are the orthogonal current and DC current of the terminal current of the multi-stage permanent magnet synchronous generator in the rotor reference system respectively; is the electrical angular velocity; is the synchronous motor resistance; is the magnetic flux connected to the stator winding; is the DC bus voltage, also expressed here as the battery voltage; It is the control signal of the wind electronic system; is the moment of inertia of the wind wheel; is the stator inductance; is the mechanical torque of the wind subsystem; is the pole pair number of the fan; The mathematical model of the wind turbine subsystem is simplified as follows:

[0009] in, and They are all nonlinear vector functions.

[0010] Furthermore, the mathematical model of the photovoltaic power generation system includes:

[0011] in, is the state vector of the photovoltaic power generation system; is the terminal voltage of the PV array; The current injected into the DC bus for the photovoltaic power generation system; and for The capacitor and inductor of the converter; is the control signal; Represents the output current of the photovoltaic subsystem; Represents the number of PV panels connected in parallel in the PV array; Represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; represents the photocurrent under reference light intensity; Represents the reverse saturation current of the photovoltaic cell; and are the output current and voltage of the photovoltaic panel, respectively; represents the electron charge constant; represents the Boltzmann constant; is the absolute temperature of the photovoltaic cell; for The bias coefficient of the junction; is the series resistance; The mathematical model of the photovoltaic power generation system is simplified as follows:

[0012] in, , is a nonlinear vector function, is a nonlinear scalar function.

[0013] Furthermore, the mathematical model of the battery energy storage subsystem includes:

[0014] in, is the terminal voltage of the battery; is the output current of the wind power electronic system; The current injected into the DC bus for the photovoltaic power generation system; is a voltage source; is the equivalent series resistance of the battery; is the equivalent capacitance of the battery.

[0015] Furthermore, the expression of the sliding mode variable structure controller of the wind subsystem is:

[0016] in, and They are the switching functions designed for the wind power generation subsystem when the wind power is sufficient and insufficient respectively; and is the designed constant factor.

[0017] Furthermore, the expression of the sliding mode variable structure controller of the photovoltaic subsystem is:

[0018] in, and They are the switching functions designed for the photovoltaic power generation system when the wind is sufficient and insufficient.

[0019] Furthermore, the distributed coordination controller of the wind power generation subsystem includes:

[0020] in, Represents the predicted state of the wind power generation electronic system at the future moment, is the economic objective function designed for wind power systems, Represents a sampling interval The maximum incremental change allowed for the wind power system within For the prediction time domain, , for The state measurement value at the moment, defined For the above optimization problem The optimal solution for economic optimization at all times.

[0021] Furthermore, the coordination controller of the photovoltaic power generation subsystem includes:

[0022] in, Represents the predicted state quantity of the photovoltaic power generation system at the future moment, is the economic objective function designed for the photovoltaic subsystem, Represents a sampling interval The maximum incremental change allowed for the PV subsystem within For the prediction time domain, , for The state measurement value at the moment, defined For the above optimization problem The optimal solution for economic optimization at all times.

[0023] Accordingly, the present invention also discloses a microgrid coordinated control system based on bidirectional iterative communication, comprising: Model building module, used to analyze the working principles of wind power generation unit, photovoltaic power generation unit and battery energy storage unit, and to establish mathematical models of wind power generation subsystem, photovoltaic power generation subsystem and battery energy storage subsystem according to the dynamic characteristics of each unit; The sub-controller design module is used to design the sliding mode variable structure controller of the wind subsystem and the sliding mode variable structure controller of the photovoltaic subsystem in turn for the wind power generation subsystem and the photovoltaic generation subsystem under all working conditions based on the sliding mode control theory; A prediction controller design module is used to establish an objective function by combining the process control performance and economic performance of the system, and to design a distributed economic model prediction controller at the coordination layer, wherein the distributed economic model prediction controller includes a distributed coordination controller of a wind power generation subsystem and a coordination controller of a photovoltaic power generation subsystem; The interactive communication module is used to perform two-way iterative communication and information interaction through the coordination layer, and use the iteratively optimized output of the distributed economic model prediction controller as the reference input of each subsystem controller of the microgrid to coordinate the unit output of each subsystem of the microgrid accordingly.

[0024] Correspondingly, the present invention also discloses a microgrid coordination control device based on bidirectional iterative communication, comprising: Memory for storing computer programs; A processor is used to implement the steps of the microgrid coordinated control method based on bidirectional iterative communication as described in any one of the above items when executing the computer program.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a microgrid coordinated control method, system and device based on two-way iterative communication, and designs a distributed microgrid coordinated control strategy for the power balance and economic dispatch problems of new energy microgrids. First, based on the highly nonlinear characteristics of wind power generation systems and solar power generation systems, sliding mode variable structure controllers for each wind and solar subsystem are designed under all working conditions. Then, on the basis of considering the balance between supply and demand, economic factors are introduced, and a distributed coordinated controller based on two-way iterative communication is designed in the coordination layer by constructing a reasonable objective function. The coordination layer will use the output of iterative optimization as the reference input of each subsystem controller to coordinate the unit output of the subsystem accordingly.

[0026] The present invention introduces economic factors on the basis of considering the balance between supply and demand, and designs a distributed economic coordination controller based on two-way iterative communication in the coordination layer by constructing a reasonable economic objective function. The coordination layer performs continuous iterative interactions based on Nash equilibrium to obtain the optimal power output reference trajectory. In turn, the unit output of each subsystem can be coordinated more accurately, and compared with the traditional power tracking control method, its economic performance is significantly improved. The present invention can effectively improve the reliability, interactivity and economy of wind, solar and storage coordinated control.

[0027] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0029] Figure 1 It is a method flow chart of a specific implementation mode of the present invention.

[0030] Figure 2 It is a system structure diagram of a specific implementation mode of the present invention.

[0031] Figure 3 It is a case simulation diagram of a specific implementation mode of the present invention. DETAILED DESCRIPTION

[0032] The specific implementation of the present invention is described below with reference to the accompanying drawings.

[0033] like Figure 1 A microgrid coordinated control method based on bidirectional iterative communication is shown, which specifically includes the following steps: S1: Analyze the working principles of wind power generation unit, photovoltaic power generation unit and battery energy storage unit, and establish the mathematical model of wind power generation subsystem, the mathematical model of photovoltaic power generation subsystem and the mathematical model of battery energy storage subsystem according to the dynamic characteristics of each unit.

[0034] In a specific implementation, the microgrid power generation system is composed of three independent subsystems, specifically: a wind power generation subsystem, a photovoltaic power generation subsystem and a battery energy storage subsystem.

[0035] The mathematical model of the wind turbine subsystem established according to the working principle of the wind turbine subsystem is:

[0036] in, is the state vector of the wind electronic system; and are the orthogonal current and DC current of the terminal current of the multi-stage permanent magnet synchronous generator in the rotor reference system respectively; is the electrical angular velocity; is the synchronous motor resistance; is the magnetic flux connected to the stator winding; is the DC bus voltage, also expressed here as the battery voltage; It is the control signal of the wind electronic system, used to adjust the duty cycle of the DC / DC converter (In this special topology, , is the winding ratio in the transformer in the DC / DC converter); is the moment of inertia of the wind wheel; is the stator inductance; is the mechanical torque of the wind subsystem; is the pole pair number of the fan.

[0037] For the sake of simplicity, the above model can be expressed as follows:

[0038] in, and They are all nonlinear vector functions, and their explicit forms are omitted for simplicity.

[0039] In order to facilitate the selection of the subsequent switching function, the power generation of the wind turbine subsystem under the optimal blade tip speed ratio operating state is introduced. The specific expression is as follows:

[0040] Among them, the first term is the maximum mechanical power captured by the wind power generation system, and the second term represents the power loss caused by the stator resistance.

[0041] The mathematical model of the photovoltaic power generation system established according to the working principle of the photovoltaic power generation system is:

[0042] in is the state vector of the photovoltaic power generation system; is the terminal voltage of the PV array; The current injected into the DC bus for the photovoltaic power generation system; and for The capacitor and inductor of the converter; is the control signal (switch control); Represents the output current of the photovoltaic subsystem; Represents the number of PV panels connected in parallel in the PV array; Represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; represents the photocurrent under reference light intensity; Represents the reverse saturation current of the photovoltaic cell; and are the output current and voltage of the photovoltaic panel, respectively; represents the electron charge constant; represents the Boltzmann constant; is the absolute temperature of the photovoltaic cell; for The junction bias coefficient (varies with the battery structure, probably between 1-5); is the series resistance.

[0043] For the sake of simplicity, the above model can be expressed as follows:

[0044] in, and They are all nonlinear vector functions, and their explicit forms are omitted for simplicity.

[0045] In order to enable smooth switching of working modes under all working conditions, this step still introduces the operating characteristics of the photovoltaic system at the maximum power point:

[0046] Then, the maximum power of the photovoltaic subsystem can be expressed as:

[0047] The mathematical model of the battery energy storage subsystem established according to the working principle of the battery energy storage system is:

[0048] in, is the terminal voltage of the battery; is the output current of the wind power electronic system; The current injected into the DC bus for the photovoltaic power generation system; is a voltage source; is the equivalent series resistance of the battery; is the equivalent capacitance of the battery.

[0049] S2: Based on the sliding mode control theory, a sliding mode variable structure controller for the wind subsystem and a sliding mode variable structure controller for the photovoltaic subsystem are designed for the wind power generation subsystem and the photovoltaic generation subsystem respectively under all working conditions.

[0050] In a specific implementation, based on the sliding mode control (variable structure control) theory, the underlying sub-controllers are designed for the wind power generation sub-system and the photovoltaic power generation sub-system in turn under all operating conditions (sufficient or insufficient wind power, sufficient or insufficient sunlight).

[0051] S201: Design a sliding mode variable structure controller for the wind subsystem as the sliding mode variable structure controller of the wind subsystem.

[0052] When the wind energy is sufficient, the power generation capacity of the wind power system can meet the external load demand, and it only needs to track the given power. Based on this working mode, the switching function can be selected as:

[0053] in, is a given reference power. And the transverse condition is:

[0054] The control signal is selected as:

[0055] in, and is the designed constant factor. And:

[0056] When the wind energy is insufficient, the power generation capacity of the wind power system cannot meet the external load demand, and the wind turbine will operate at maximum power. Based on this working mode, the switching function can be selected as:

[0057] The horizontal conditions are:

[0058] The control signal is selected as:

[0059] in, and is the designed constant factor. And:

[0060] S202: Design a sliding mode variable structure controller for the photovoltaic subsystem as a sliding mode variable structure controller for the photovoltaic subsystem.

[0061] When the sunlight is sufficient, the photovoltaic system's power generation capacity can meet the external load demand, and it only needs to track the given power. Based on this working mode, the switching function is selected as:

[0062] The corresponding control strategy is:

[0063] When the sunlight is insufficient, the photovoltaic system's power generation capacity cannot meet the external load demand and will always operate at the maximum power operating point. Based on this working mode, the switching function is selected as:

[0064] The corresponding control strategy is:

[0065] S3: Establish an objective function in combination with the process control performance and economic performance of the system, and design a distributed economic model prediction controller at the coordination layer, wherein the distributed economic model prediction controller includes a distributed coordination controller of a wind power generation subsystem and a coordination controller of a photovoltaic power generation subsystem.

[0066] In a specific implementation, a distributed coordination controller is designed at the coordination layer by establishing an objective function that considers supply and demand balance and economic performance. An iterative interactive communication working mechanism is introduced at the coordination layer, that is, each subsystem iteratively interacts based on a two-way communication channel, and then solves the optimization problem.

[0067] As an example, the specific process of this step is as follows: S301: Optimize the objective function design.

[0068] The control goal of the microgrid in the present invention is to make full use of wind and solar power generation to meet the external load demand while considering some economic factors. In this process, the method designs a suitable economic objective function, and the main principles considered are as follows (the performance indicators of the objective function can be selected according to demand): Supply and demand balance of microgrid: The imbalance between supply and demand of microgrid system will reduce the power quality and affect the stability of the system. Therefore, ensuring the balance between supply and demand is one of the important components of the control objective.

[0069]

[0070] in, is a constant coefficient, is the external load demand, and They are the real-time power generation of the wind subsystem and the photovoltaic subsystem respectively.

[0071] Wind power generation is prioritized: Since wind power is more economical than photovoltaic power, we mainly use wind power generation system and photovoltaic power generation system as a supplement. When wind power supply is insufficient, photovoltaic power generation will be used to supplement the power generation.

[0072]

[0073] in, is a constant coefficient.

[0074] Mechanical losses of wind turbines: By suppressing drastic changes in mechanical torque, gearbox wear can be reduced and the service life of the turbine can be increased.

[0075]

[0076] in, is a constant coefficient, is the mechanical torque of the fan.

[0077] Battery benefits and costs: The economic performance of the battery pack also needs to be focused on. Avoid frequent charging and discharging of the battery, optimize its use, and extend its service life.

[0078]

[0079] in, and is a constant coefficient, and They are the changing power and state of charge of the battery respectively.

[0080] Maintain the stability of the DC bus voltage: The DC bus voltage is directly related to the safety of the microgrid. When the difference between the DC bus voltage and the standard value is too large, the power quality of the microgrid cannot be guaranteed, leading to safety problems and reducing economic efficiency. Therefore, it is necessary to reduce the DC bus voltage fluctuation and try to keep it close to the DC bus reference value.

[0081]

[0082] in, is a constant coefficient, is the DC bus voltage, is the DC bus voltage reference value.

[0083] In summary, microgrid optimization problems contain multiple requirements. In order to make each objective as optimal as possible, coordination and balance between objectives need to be achieved. The most common method is the linear weighted method, which normalizes each component of the objective function by selecting an appropriate weighting factor. The specific expression is as follows:

[0084] in, is the corresponding weight factor.

[0085] S302: Set optimization problem 1 as a distributed coordination controller of the wind power generation subsystem, as follows:

[0086] in, Represents the predicted state of the wind power generation electronic system at the future moment, is the economic objective function designed for wind power systems, Represents a sampling interval The maximum incremental change allowed for the wind power system within For the prediction time domain, , for The state measurement value at a certain moment. Definition For the above optimization problem The optimal solution for economic optimization at all times.

[0087] Set the optimization problem 2 as the coordinated controller of the photovoltaic subsystem as follows:

[0088] in, Represents the predicted state quantity of the photovoltaic power generation system at the future moment, is the economic objective function designed for the photovoltaic subsystem, Represents a sampling interval The maximum incremental change allowed for the PV subsystem within For the prediction time domain, , for The state measurement value at a certain moment. Definition For the above optimization problem The optimal solution for economic optimization at all times.

[0089] It should be noted that the core of the coordinated control strategy based on bidirectional iterative communication proposed in the present invention is to evaluate two optimization problems in parallel and iterate to improve the closed-loop performance of the system. In this architecture, a bidirectional communication channel is used to interconnect two distributed controllers. When a new state measurement is available at the sampling time, the distributed controllers will evaluate each other and propagate their future reference trajectories. Based on the newly received and previously retained reference trajectories, the parallel evaluation and propagation process will be performed again until the preset threshold or the maximum number of iterations is reached.

[0090] S4: Bidirectional iterative communication and information interaction are carried out through the coordination layer, and the output of the iterative optimization of the distributed economic model prediction controller is used as the reference input of the controller of each subsystem of the microgrid to coordinate the unit output of each subsystem of the microgrid accordingly.

[0091] In a specific implementation, the output after the coordination layer optimization is used as the reference input of each subsystem controller to coordinate the unit output of the subsystem accordingly for tracking. This forms a control strategy of upper layer optimization-lower layer tracking, and realizes process control and economic optimization of the microgrid system.

[0092] The present invention discloses a microgrid coordinated control method based on two-way iterative communication. First, based on the highly nonlinear characteristics of wind power generation systems and solar power generation systems, sliding mode variable structure controllers of wind and solar subsystems are designed under all working conditions. Then, on the basis of considering the balance between supply and demand, economic factors are introduced, and a distributed coordinated controller based on two-way iterative communication is designed in the coordination layer by constructing a reasonable objective function. The coordination layer will use the output of iterative optimization as the reference input of each subsystem controller to coordinate the unit output of the subsystem accordingly. This method can effectively improve the reliability, interactivity and economy of microgrid coordinated control.

[0093] Correspondingly, such as Figure 2 As shown, the present invention also discloses a microgrid coordinated control system based on bidirectional iterative communication, including: a model building module 1, a sub-controller design module 2, a predictive controller design module 3 and an interactive communication module 4.

[0094] Model building module 1 is used to analyze the working principles of wind power generation units, photovoltaic power generation units and battery energy storage units, and to establish mathematical models of wind power generation subsystems, photovoltaic power generation subsystems and battery energy storage subsystems according to the dynamic characteristics of each unit.

[0095] The sub-controller design module 2 is used to design a sliding mode variable structure controller of the wind subsystem and a sliding mode variable structure controller of the photovoltaic subsystem in turn for the wind power generation subsystem and the photovoltaic generation subsystem under all working conditions based on the sliding mode control theory.

[0096] The prediction controller design module 3 is used to establish an objective function by combining the process control performance and economic performance of the system, and to design a distributed economic model prediction controller at the coordination layer. The distributed economic model prediction controller includes a distributed coordination controller of a wind power generation subsystem and a coordination controller of a photovoltaic power generation subsystem.

[0097] The interactive communication module 4 is used to perform two-way iterative communication and information interaction through the coordination layer, and use the iteratively optimized output of the distributed economic model prediction controller as the reference input of each subsystem controller of the microgrid to coordinate the unit output of each subsystem of the microgrid accordingly.

[0098] The specific implementation of the microgrid coordinated control system based on bidirectional iterative communication in this embodiment is basically the same as the specific implementation of the microgrid coordinated control method based on bidirectional iterative communication described above, and will not be repeated here.

[0099] Further, if Figure 3 As shown, the present invention also discloses a simulation analysis case of a wind-solar-storage coordinated control method based on two-way iterative communication, including the following: Figure 3 The solid line in represents the load demand in a specific area and time period, the dotted line represents the unit output of the wind subsystem under the corresponding control constraints, the dotted line represents the unit output of the photovoltaic subsystem under the corresponding control constraints, and the dot-dash line represents the power output of the energy storage subsystem. When the output power is positive, it represents discharge, and when it is negative, it represents charging. In the control process, it can be seen that in the face of dynamic changes in load demand, the subsystems coordinate with each other and can provide a good supply, and the energy storage system can give full play to its charging and discharging characteristics to store too much energy or emergency power shortages.

[0100] Correspondingly, the present invention also discloses a microgrid coordination control device based on bidirectional iterative communication, comprising: Memory for storing computer programs; A processor is used to implement the steps of the microgrid coordinated control method based on bidirectional iterative communication as described in any one of the above items when executing the computer program.

[0101] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium such as a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, etc., which can store program codes, including several instructions to enable a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The same and similar parts between the various embodiments in this specification can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0102] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each module may exist physically separately, or two or more modules may be integrated into one unit.

[0105] Similarly, each processing unit in each embodiment of the present invention may be integrated into one functional module, or each processing unit may exist physically, or two or more processing units may be integrated into one functional module.

[0106] The present invention will be further described with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the application.

Claims

1. A microgrid coordinated control method based on bidirectional iterative communication, characterized in that: include: Analyze the working principles of wind power generation unit, photovoltaic power generation unit and battery energy storage unit, and establish mathematical models of wind power generation subsystem, photovoltaic power generation subsystem and battery energy storage subsystem according to the dynamic characteristics of each unit; Based on the sliding mode control theory, the sliding mode variable structure controller of the wind subsystem and the sliding mode variable structure controller of the photovoltaic subsystem are designed for the wind power generation subsystem and the photovoltaic generation subsystem respectively under all working conditions; An objective function is established by combining the process control performance and economic performance of the system, and a distributed economic model prediction controller is designed at the coordination layer, wherein the distributed economic model prediction controller includes a distributed coordination controller of a wind power generation subsystem and a coordination controller of a photovoltaic power generation subsystem; Bidirectional iterative communication and information interaction are carried out through the coordination layer, and the output of the iterative optimization of the distributed economic model predictive controller is used as the reference input of the controller of each subsystem of the microgrid to coordinate the unit output of each subsystem of the microgrid accordingly.

2. The microgrid coordinated control method based on bidirectional iterative communication according to claim 1 is characterized in that: The mathematical model of the wind power generation subsystem includes: in, is the state vector of the wind electronic system; and are the orthogonal current and DC current of the terminal current of the multi-stage permanent magnet synchronous generator in the rotor reference system respectively; is the electrical angular velocity; is the synchronous motor resistance; is the magnetic flux connected to the stator winding; is the DC bus voltage, also expressed here as the battery voltage; It is the control signal of the wind electronic system; is the moment of inertia of the wind wheel; is the stator inductance; is the mechanical torque of the wind subsystem; is the pole pair number of the fan; The mathematical model of the wind turbine subsystem is simplified as follows: in, and They are all nonlinear vector functions.

3. The microgrid coordinated control method based on bidirectional iterative communication according to claim 2 is characterized in that: The mathematical model of the photovoltaic power generation system includes: in, is the state vector of the photovoltaic power generation system; is the terminal voltage of the PV array; The current injected into the DC bus for the photovoltaic power generation system; and for The capacitor and inductor of the converter; is the control signal; Represents the output current of the photovoltaic subsystem; Represents the number of PV panels connected in parallel in the PV array; Represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; represents the photocurrent under reference light intensity; Represents the reverse saturation current of the photovoltaic cell; and are the output current and voltage of the photovoltaic panel, respectively; represents the electron charge constant; represents the Boltzmann constant; is the absolute temperature of the photovoltaic cell; for The bias coefficient of the junction; is the series resistance; The mathematical model of the photovoltaic power generation system is simplified as follows: in, , is a nonlinear vector function, is a nonlinear scalar function.

4. The microgrid coordinated control method based on bidirectional iterative communication according to claim 3 is characterized in that: The mathematical model of the battery energy storage subsystem includes: in, is the terminal voltage of the battery; is the output current of the wind power electronic system; The current injected into the DC bus for the photovoltaic power generation system; is a voltage source; is the equivalent series resistance of the battery; is the equivalent capacitance of the battery.

5. The microgrid coordinated control method based on bidirectional iterative communication according to claim 4 is characterized in that: The expression of the sliding mode variable structure controller of the wind subsystem is: in, and They are the switching functions designed for the wind power generation subsystem when the wind power is sufficient and insufficient respectively; and is the designed constant factor.

6. The microgrid coordinated control method based on bidirectional iterative communication according to claim 5 is characterized in that: The expression of the sliding mode variable structure controller of the photovoltaic subsystem is: in, and They are the switching functions designed for the photovoltaic power generation system when the wind is sufficient and insufficient.

7. The microgrid coordinated control method based on bidirectional iterative communication according to claim 6 is characterized in that: The distributed coordination controller of the wind power generation subsystem includes: in, Represents the predicted state of the wind power generation electronic system at the future moment, is the economic objective function designed for wind power systems, Represents a sampling interval The maximum incremental change allowed for the wind power system within For the prediction time domain, , for The state measurement value at the moment is defined as For the above optimization problem The optimal solution for economic optimization at all times.

8. The microgrid coordinated control method based on bidirectional iterative communication according to claim 7 is characterized in that: The coordination controller of the photovoltaic power generation subsystem includes: in, Represents the predicted state quantity of the photovoltaic power generation system at the future moment, is the economic objective function designed for the photovoltaic subsystem, Represents a sampling interval The maximum incremental change allowed for the PV subsystem within For the prediction time domain, , for The state measurement value at the moment is defined as For the above optimization problem The optimal solution for economic optimization at all times.

9. A microgrid coordinated control system based on bidirectional iterative communication, characterized in that: The system adopts the microgrid coordinated control method based on bidirectional iterative communication as claimed in any one of claims 1 to 8; The system comprises: Model building module, used to analyze the working principles of wind power generation unit, photovoltaic power generation unit and battery energy storage unit, and to establish mathematical models of wind power generation subsystem, photovoltaic power generation subsystem and battery energy storage subsystem according to the dynamic characteristics of each unit; A sub-controller design module is used to design a sliding mode variable structure controller for the wind subsystem and a sliding mode variable structure controller for the photovoltaic subsystem in turn for the wind power generation subsystem and the photovoltaic generation subsystem under all working conditions based on the sliding mode control theory; A prediction controller design module is used to establish an objective function by combining the process control performance and economic performance of the system, and to design a distributed economic model prediction controller at the coordination layer, wherein the distributed economic model prediction controller includes a distributed coordination controller of a wind power generation subsystem and a coordination controller of a photovoltaic power generation subsystem; The interactive communication module is used to perform two-way iterative communication and information interaction through the coordination layer, and use the iteratively optimized output of the distributed economic model prediction controller as the reference input of each subsystem controller of the microgrid to coordinate the unit output of each subsystem of the microgrid accordingly.

10. A microgrid coordinated control device based on bidirectional iterative communication, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the microgrid coordinated control method based on bidirectional iterative communication as described in any one of claims 1 to 8 when executing the computer program.

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