Micro-grid distributed economic model prediction control method and related device

Abstract

The invention discloses a micro-grid distributed economic model prediction control method and a related device, and belongs to the technical field of micro-grid system control. The method comprises the following steps: firstly, establishing a wind power generation subsystem mathematical model, a photovoltaic power generation subsystem mathematical model and a storage battery energy storage subsystem mathematical model according to dynamic characteristics of a wind power generation subsystem, a photovoltaic power generation subsystem and a storage battery energy storage subsystem; based on a sliding mode control theory, a bottom layer sliding mode variable structure controller is designed for each wind and light subsystem under all working conditions; establishing an economic objective function of the micro-grid, and designing a distributed economic model prediction controller in a supervision layer according to the economic objective function; switching of the first sliding mode variable structure controller and the second sliding mode variable structure controller is controlled through the distributed economic model prediction controller, and the optimal operation power of the wind power generation subsystem, the optimal operation power of the photovoltaic power generation subsystem and the optimal operation power of the storage battery energy storage subsystem are output respectively.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid system control, and relates to a microgrid distributed economic model predictive control method and related devices. Background Art

[0002] Energy is the basis of human survival. Electricity, as an important secondary energy source, has a significant and irreplaceable impact on social development. However, the traditional power system based on fossil fuels and coal as the main energy sources faces many challenges, including resource depletion, environmental pollution and climate change. Therefore, the development of a new power system based on renewable energy has become an inevitable trend.

[0003] However, behind the large-scale installation, the problem of renewable energy consumption has become increasingly prominent. The output of new energy such as wind power and photovoltaic power is characterized by volatility and randomness, and the temporal and spatial distribution of electricity is extremely uneven, with abundance and shortage intertwined, bringing challenges to sufficiency; the weak support of new energy has led to the intensification of the "hollowing out" of the power grid, and the intensity of the impact caused by failures has increased. The regulation and tolerance of new energy are insufficient compared to synchronous machines, which brings safety challenges; the cost of new energy has decreased, but the regulation and safety costs of system matching have increased significantly. The high-power scenario of new energy requires the coordination of multiple industries and multiple systems, which brings economic challenges. Therefore, in the context of low-carbon and green energy transformation, in order to meet the user's requirements for high-quality and high-reliability electricity 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 randomness, 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 safety, 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] The purpose of the present invention is to provide a microgrid distributed economic model predictive control method and related devices to solve the technical problems in the prior art that the microgrid system operates unstably under complex environmental conditions, is difficult to meet cloud electricity demand, and affects the service life of power generation equipment.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a microgrid distributed economic model predictive control method, comprising the following steps:

[0008] Establish a mathematical model of the wind power generation subsystem according to the dynamic characteristics of the wind power generation subsystem, establish a mathematical model of the photovoltaic power generation subsystem according to the dynamic characteristics of the photovoltaic power generation subsystem, and establish a mathematical model of the battery energy storage subsystem according to the dynamic characteristics of the battery energy storage subsystem;

[0009] Based on the sliding mode control theory under all working conditions, the first sliding mode variable structure controller is designed according to the mathematical model of the wind power generation subsystem; the second sliding mode variable structure controller is designed according to the mathematical model of the photovoltaic power generation subsystem and the mathematical model of the battery energy storage subsystem;

[0010] Establish the economic objective function of the microgrid and design a distributed economic model predictive controller at the supervisory layer based on the economic objective function;

[0011] The switching of the first sliding mode variable structure controller and the second sliding mode variable structure controller is controlled by a distributed economic model predictive controller, and the optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem are output respectively.

[0012] Furthermore, the mathematical model of the wind power generation subsystem, photovoltaic power generation subsystem and battery energy storage subsystem is abbreviated as:

[0013]

[0014] Among them, f ii , g ii , f ij and g ij are all nonlinear vector functions, x i (t) is the state of the ith subsystem, x j (t) is the state of the jth subsystem, u i (t) is the input of the ith subsystem, u j (t) is the input of the jth subsystem.

[0015] Furthermore, the step of designing a first sliding mode variable structure controller according to the mathematical model of the wind turbine subsystem specifically includes:

[0016] The first sliding mode variable structure controller is designed so that when the wind energy is sufficient, the wind turbine subsystem tracks the given power; when the wind energy is insufficient, the wind turbine of the wind turbine subsystem operates at the maximum power.

[0017] Furthermore, the step of designing a second sliding mode variable structure controller according to the mathematical model of the photovoltaic power generation system specifically includes:

[0018] The second sliding mode variable structure controller is designed so that when the sunlight is sufficient, the photovoltaic power generation subsystem tracks the given power; when the sunlight is insufficient, the photovoltaic power generation subsystem operates at the maximum power.

[0019] Furthermore, the step of establishing the economic objective function specifically includes:

[0020] Establish the microgrid supply and demand balance function, the specific expression is:

[0021] l 1 =α(P t -P w -P s ) 2

[0022] Among them, l 1 represents the microgrid supply and demand balance function; α is a constant coefficient; P t is the external load demand; P w is the real-time power generation of the wind subsystem; P s The real-time power generation of the photovoltaic subsystem;

[0023] Establish a wind power generation function, the specific expression is:

[0024]

[0025] Among them, l 2 is the wind power generation function; β is a constant coefficient; P s is the real-time power generation of the photovoltaic subsystem; the mechanical loss function of the wind turbine is established, and the specific expression is:

[0026] l 3 =σΔT t

[0027] Among them, l 3 is the mechanical loss function of the fan; σ is a constant coefficient; T t is the mechanical torque of the fan;

[0028] Establish the battery benefit cost function, the specific expression is:

[0029]

[0030] Among them, l4 is the battery benefit cost function; ε and η are constant coefficients; ΔP b is the changing power of the battery; Soc is the charge state of the battery;

[0031] Establish the DC bus voltage function, the specific expression is:

[0032] l 5 =δ(v b -v 0 ) 2

[0033] Among them, l 5 DC bus voltage function; δ is a constant coefficient; v b is the DC bus voltage; v 0 is the DC bus voltage reference value;

[0034] The microgrid supply and demand balance function, wind power generation function, wind turbine mechanical loss function, battery benefit cost function and DC bus voltage function are linearly weighted to finally obtain the economic objective function of the microgrid, which is specifically expressed as follows:

[0035] L=R 1 l 1 +R 2 l 2 +R 3 l 3 +R 4 l 4 +R 5 l 5

[0036] Among them, R 1 is the weight factor of the microgrid supply and demand balance function; R 2 is the weight factor of wind power generation function; R 3 is the weight factor of the wind turbine mechanical loss function; R 4 is the weight factor of the benefit-cost function of the battery; R 5 is the weight factor of the DC bus voltage function.

[0037] Furthermore, the expression of the distributed economic model prediction controller is:

[0038]

[0039] u i (τ)∈U i

[0040]

[0041] if V(x(t k ))≤ρi '

[0042]

[0043] ifρ i ≥V i (x(t k ))>ρ i '

[0044] Among them, τ is the integral variable; ρ′ i Ideal feasible domain The radius of i is the perturbation feasible region The radius of is the state measurement value of subsystem i at time k, and is the initial value of the state variable of subsystem i during the rolling optimization process; The control sequence obtained by solving the optimization problem under the ideal model; is the constructed Lyapunov function.

[0045] When the state variable is monitored to be in the ideal feasible region, ρ' When the system controller switches to the distributed economic model predictive control; when the state variable is detected to be in the ideal feasible domain Ω ρ' and the perturbation feasible region Ω ρ When the system control input is switched to Drive the system state into the ideal feasible domain Ω ρ' .

[0046] Furthermore, it also includes:

[0047] The optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem are respectively used as reference inputs of each subsystem controller to track the unit output of the subsystem.

[0048] In a second aspect, the present invention provides a microgrid distributed economic model predictive control system, comprising:

[0049] A mathematical model building module is used to build a mathematical model of a wind power generation subsystem according to the dynamic characteristics of the wind power generation subsystem, build a mathematical model of a photovoltaic power generation subsystem according to the dynamic characteristics of the photovoltaic power generation subsystem, and build a mathematical model of a battery energy storage subsystem according to the dynamic characteristics of the battery energy storage subsystem;

[0050] The sliding mode variable structure controller design module is used to design the first sliding mode variable structure controller based on the sliding mode control theory under all working conditions and according to the mathematical model of the wind power generation subsystem; the second sliding mode variable structure controller is designed according to the mathematical model of the photovoltaic power generation subsystem and the mathematical model of the battery energy storage subsystem;

[0051] The prediction controller design module is used to establish the economic objective function of the microgrid and design the distributed economic model prediction controller at the supervision layer according to the economic objective function;

[0052] The switching control module is used to control the switching of the first sliding mode variable structure controller and the second sliding mode variable structure controller through a distributed economic model prediction controller, and output the optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem respectively.

[0053] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention discloses a distributed economic model predictive control method and related devices for a microgrid. By constructing accurate mathematical models of wind power generation subsystems, photovoltaic power generation subsystems, and battery energy storage subsystems, an accurate description of the dynamic characteristics of each key component in the microgrid is achieved. The method uses sliding mode control theory to design a first mode variable structure controller and a second sliding mode variable structure controller under all operating conditions, which effectively improves the adaptability and robustness of the system to complex operating environments and ensures the stable operation of the microgrid under different operating conditions. And by establishing an objective function that considers supply and demand balance and economic performance, a distributed economic model predictive controller is designed at the supervisory layer to ensure that the system can better perform process control and economic optimization under complex environmental conditions; the present invention introduces a switching control strategy, expands the controller's economic optimization freedom, and ensures the stability of the closed-loop system.

[0057] Furthermore, the present invention introduces the switching control strategy based on Lyapunov technology into the supervision layer, which can ensure that the system state always remains in the stable domain during the controller switching process, effectively avoiding the system instability problem that may be caused by improper controller switching. This further improves the operation stability and reliability of the microgrid under various complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 is a flow chart of the method of the present invention;

[0060] Figure 2 is a schematic diagram of the system of the present invention;

[0061] Figure 3 This is a schematic diagram of the overall structure of a microgrid power generation system according to an embodiment of the present invention;

[0062] Figure 4 This is a schematic diagram of a wind power generation electronic system according to an embodiment of the present invention;

[0063] Figure 5 This is a schematic diagram of a photovoltaic power generation system according to an embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of a battery energy storage system according to an embodiment of the present invention;

[0065] Figure 7 This is a diagram of the distributed economic model predictive control structure of an embodiment of the present invention;

[0066] Figure 8 The external environment conditions and corresponding load requirements of the embodiment of the present invention;

[0067] Fig. 9 The power output of each power generation subsystem of the microgrid according to the embodiment of the present invention;

[0068] Fig.10 The severe external environment changes and corresponding load requirements of the embodiments of the present invention;

[0069] Fig.11 The load tracking situation of the microgrid power generation system under the harsh environmental conditions of the embodiment of the present invention;

[0070] Fig.12 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0071] 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.

[0072] 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.

[0073] 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.

[0074] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0075] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0076] In the description of the embodiments 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 connection 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.

[0077] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0078] See also Figure 1 The embodiment of the present invention discloses a microgrid distributed economic model predictive control method, comprising the following steps:

[0079] S1, establishing a mathematical model of a wind power generation subsystem according to the dynamic characteristics of the wind power generation subsystem, establishing a mathematical model of a photovoltaic power generation subsystem according to the dynamic characteristics of the photovoltaic power generation subsystem, and establishing a mathematical model of a battery energy storage subsystem according to the dynamic characteristics of the battery energy storage subsystem;

[0080] The microgrid power generation system consists of three independent subsystems: wind power generation subsystem, photovoltaic power generation subsystem, and battery energy storage subsystem. Figure 3 shown.

[0081] The mathematical model of the wind power generation subsystem, photovoltaic power generation subsystem and battery energy storage subsystem is abbreviated as:

[0082]

[0083] Among them, f ii , g ii , f ij and g ij are all nonlinear vector functions, x i (t) is the state of the ith subsystem, x j (t) is the state of the jth subsystem, u i (t) is the input of the ith subsystem, u j (t) is the input of the jth subsystem.

[0084] Specifically, the construction process of each model is as follows:

[0085] S101, according to the wind power generation electronic system (such as attached Figure 4 The working principle of the mathematical model is as follows:

[0086]

[0087] Among them, x w =[i q i d ω e ] T is the state vector of the wind electronic system; i q and i d 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; ω e is the electrical angular velocity; R s is the synchronous motor resistance; φ m is the magnetic flux connected to the stator winding; v b is the DC bus voltage, also expressed here as the battery voltage; uw is the control signal of the wind electronic system, which is used to adjust the duty cycle δ of the DC / DC converter (in this special topology, u w =k tr / δ,k tr is the winding ratio in the transformer of the DC / DC converter); J is the moment of inertia of the wind wheel; L is the stator inductance; T t is the mechanical torque of the wind subsystem; P is the pole pair number of the wind turbine.

[0088] 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:

[0089]

[0090] 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.

[0091] S102, according to the photovoltaic power generation system (such as attached Figure 5 The working principle of the mathematical model is as follows:

[0092]

[0093] Among them, x s =[v pv i s ] T is the state vector of the photovoltaic power generation system; v pv is the terminal voltage of the photovoltaic array; i s The current injected into the DC bus of the photovoltaic power generation system; C and L are the capacitance and inductance of the DC / DC converter; u s is the control signal (switch control); i pv Represents the output current of the photovoltaic subsystem; n p Represents the number of photovoltaic panels connected in parallel in the photovoltaic array; n s Represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; I ph Represents the photocurrent under reference light intensity; I rs Represents the reverse saturation current of the photovoltaic cell; i pv and v pv are the output current and voltage of the photovoltaic panel respectively; q represents the electron charge constant; K represents the Boltzmann constant; T is the absolute temperature of the photovoltaic cell; A c is the bias coefficient of the PN junction (varies with the battery structure, probably between 1 and 5); R s is the series resistance.

[0094] In order to enable smooth switching of working modes under all working conditions, we still introduce the operating characteristics of the photovoltaic system at the maximum power point:

[0095]

[0096] Then, the maximum power of the photovoltaic subsystem can be expressed as:

[0097]

[0098] S103, according to the battery energy storage system (such as attached Figure 6 The working principle of the mathematical model is as follows:

[0099]

[0100] Among them, v c The terminal voltage of the battery; i w is the output current of the wind power electronic system; i s The current injected into the DC bus for the photovoltaic power generation system; E b is the voltage source; R b is the equivalent series resistance of the battery; C b is the equivalent capacitance of the battery.

[0101] S2, under all working conditions (sufficient or insufficient wind power, sufficient or insufficient sunlight), based on the sliding mode control theory, the first sliding mode variable structure controller is designed according to the mathematical model of the wind power generation subsystem; the second sliding mode variable structure controller is designed according to the mathematical model of the photovoltaic power generation subsystem and the mathematical model of the battery energy storage subsystem;

[0102] S201, designing a first sliding mode variable structure controller for the wind subsystem;

[0103] 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:

[0104]

[0105] Among them, P wref is a given reference power. And the transverse condition is:

[0106]

[0107] The control signal is selected as:

[0108]

[0109] Among them, γ and ξ max is the designed constant factor. And:

[0110]

[0111] 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:

[0112]

[0113] Among them, ω e is the electrical angular velocity; i q is the orthogonal current of the terminal current of the multi-stage permanent magnet synchronous generator in the rotor reference system; K opt is the intermediate variable; opt is the optimal tip speed ratio, C t (λ opt ) is the torque coefficient of the wind turbine, ρ is the air density, A is the area swept by the wind wheel, R is the impeller radius, and P is the number of poles of the wind turbine.

[0114] The horizontal conditions are:

[0115]

[0116] The control signal is selected as:

[0117]

[0118] Among them, γ and ξ max is the designed constant factor. And:

[0119]

[0120] S202, design the second sliding mode variable structure controller for the photovoltaic subsystem and battery energy storage subsystem

[0121] When the sunlight is sufficient, the power generation capacity of the photovoltaic system can meet the external load demand, and only the given power needs to be tracked. Based on this working mode, the switching function is selected as:

[0122] s s1 (x) = i L -i s

[0123] The corresponding control strategy is:

[0124]

[0125] 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:

[0126]

[0127] The corresponding control strategy is:

[0128]

[0129] S3, establish the economic objective function of the microgrid, and design a distributed economic model prediction controller at the supervision layer according to the economic objective function;

[0130] S301, optimize the objective function design and establish the economic objective function of the microgrid;

[0131] The control goal of the microgrid in this 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, we designed a suitable economic objective function, and the main principles considered are as follows:

[0132] 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.

[0133] l 1 =α(P t -P w -P s ) 2

[0134] Among them, α is a constant coefficient, P t is the external load demand, P w and P s They are the real-time power generation of the wind subsystem and the photovoltaic subsystem respectively.

[0135] 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.

[0136]

[0137] Where β is a constant coefficient.

[0138] 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.

[0139]

[0140] Where σ is a constant coefficient, T t is the mechanical torque of the fan.

[0141] 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.

[0142]

[0143] Among them, ε and η are constant coefficients, ΔP b and Soc are the changing power and state of charge of the battery respectively.

[0144] 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.

[0145] l 5 =δ(v b -v 0 ) 2

[0146] Among them, δ is a constant coefficient, v b is the DC bus voltage, v 0 is the DC bus voltage reference value.

[0147] 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:

[0148] L=R 1 l 1 +R 2 l 2 +R 3 l 3 +R 4 l 4 +R 5 l 5

[0149] Among them, R i (i=1,2,3,4,5) are the corresponding weight factors.

[0150] S302, optimization problem, design of distributed economic model predictive controller;

[0151] The specific expression of the distributed economic model predictive controller of the subsystem i is:

[0152]

[0153] u i (τ)∈U i

[0154]

[0155] if V(x(t k ))≤ρ i '

[0156]

[0157] ifρ i ≥V i (x(t k ))>ρ i '

[0158] Among them, τ is the integral variable; ρ' is the ideal feasible region Ω ρ' The radius of the disturbance; ρ is the feasible region of the disturbance Ω ρ The radius of x(t k ) is the state measurement value at time k, which is the initial value of the state variable of subsystem i during the rolling optimization process; The control sequence obtained by solving the optimization problem under the ideal model; is the constructed Lyapunov function.

[0159] In the control process, when the state variable is monitored to be in the ideal feasible region Ω ρ' When , the system controller will switch to the distributed economic model predictive control and obtain the corresponding control action by solving the optimization problem. In this mode, the optimization process does not need to limit the optimization problem to the stable path like the traditional model predictive control, but performs dynamic economic optimization through the preset objective function to maximize the use of the ideal feasible domain Ω ρ' When the state variable is detected to be in the ideal feasible region Ω ρ' and the perturbation feasible region Ω ρ When the system control input is switched to Its function is to make the Lyapunov function move in the direction of attenuation, thereby driving the system state into the ideal feasible domain Ω ρ' .

[0160] Next, we will discuss two types of feasible domains and control inputs. How to obtain it. First, in order to enable the controlled subsystems to optimize their economic objectives in a relatively safe and stable area, a Lyapunov function V(x) = x is defined for each subsystem. T Px, where P is a positive definite weight matrix, and the undisturbed ideal feasible domain can be solved by the following optimization problem:

[0161] ρ'=maxV(x)

[0162] stx∈X

[0163] h(x)∈U

[0164] Among them, U is the system control input constraint, X is the system state variable constraint, and similarly, the perturbation feasible domain ρ can be obtained, assuming ρ'<ρ. h(x) can be specifically described by the following expression:

[0165]

[0166] in, and is the corresponding Lie derivative of the Lyapunov function.

[0167] S4, controlling the switching of the first sliding mode variable structure controller and the second sliding mode variable structure controller through the distributed economic model prediction controller, and outputting the optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem respectively.

[0168] S5, the optimized output of the supervisory layer, the optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem are 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. On the basis of ensuring system stability, the process control and economic optimization of the microgrid system are realized, such as Figure 7 shown.

[0169] Example:

[0170] This embodiment uses a microgrid distributed economic model predictive control method to simulate a microgrid power generation system. The system parameters are shown in Table 1:

[0171] Table 1 Parameters of the microgrid power generation system in the embodiment

[0172]

[0173]

[0174] Next, we established two sets of simulation experiments to demonstrate the applicability and superiority of the proposed distributed economic model predictive control method based on switching control strategy in microgrid systems. s =1s, prediction time domain N p =3, control time domain N c =3, the maximum change value of the output power of the wind and solar subsystems is set to dP wmax =1000,dP smax= 500. The setting of the constraints of the prediction time domain and the output power change value comprehensively considers the response speed of the microgrid power generation system and the change rate of the load demand. In addition, it also takes into account the optimization time and closed-loop control performance of the distributed predictive controller.

[0175] Attached Figure 8 Represents the external environmental conditions and the corresponding load requirements. Figure 8 (a) represents the change of wind speed, Figure 8 (b) represents the change of light intensity, Figure 8 (c) represents the temperature change, Figure 8 (d) represents the external load demand.

[0176] Attached Fig. 9 Represents the power output of each power generation subsystem of the microgrid. Fig. 9 The solid line in (a) represents the total load demand, the dashed line represents the total output power of the wind and solar power generation subsystems, and the dotted line represents the power provided by the battery emergency. The subsystems coordinate with each other to meet the load demand well. Fig. 9 The dotted lines in (b) and 9(c) are the maximum output power of the wind and solar subsystems, and the solid lines are the corresponding actual output power of the wind and solar subsystems. It is not difficult to see that when the external load demand changes, the microgrid subsystems under the distributed economic model predictive control strategy based on dual-mode switching control can coordinate with each other and respond to the changing load demand better and faster, demonstrating the effectiveness of the strategy proposed in this chapter.

[0177] In order to further verify the control effect of the dual-mode distributed economic model predictive control algorithm applied to the microgrid power generation system, our simulation experiment in this section assumes that the system is under harsh environmental conditions, that is, the wind speed, light intensity and temperature are all set as random functions with high-frequency disturbances.

[0178] Attached Fig.10 Represents the harsh external environment changes and corresponding load requirements. Fig.10 (a) represents the change of wind speed, Fig.10 (b) represents the change of light intensity, Fig.10 (c) represents the temperature change, Fig.10 (d) represents the external load demand.

[0179] Attached Fig.11 Represents the load following of the microgrid generation system under harsh environmental conditions. Fig.11The solid line in (a) represents the external load demand, the dotted line represents the total output power of the wind and solar subsystems, and the dotted line represents the emergency supplementary power of the battery. It is not difficult to see that under extreme environmental conditions, the microgrid power generation system can still track the external load demand relatively well, and its curve trend is roughly similar to the previous one. This shows that the distributed economic model predictive control strategy based on switching control proposed by us can make the system robust and stable under disturbance.

[0180] See also Figure 2 The embodiment of the present invention discloses a microgrid distributed economic model predictive control system, including a mathematical model building module, a sliding mode variable structure controller design module, a predictive controller design module and a switching control module.

[0181] It should be noted that the mathematical model establishment module analyzes the working principles of the wind power generation subsystem, the photovoltaic power generation subsystem and the battery energy storage subsystem, and establishes the mathematical model of the wind power generation subsystem, the mathematical model of the photovoltaic power generation subsystem and the mathematical model of the battery energy storage subsystem according to the dynamic characteristics of each subsystem.

[0182] Specifically, the mathematical model of the wind power generation subsystem can be summarized as follows:

[0183]

[0184] Among them, x w =[i q i d ω e ] T is the state vector of the wind electronic system; i q and i d 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; ω e is the electrical angular velocity; R s is the synchronous motor resistance; φ m is the magnetic flux connected to the stator winding; v b is the DC bus voltage, also expressed here as the battery voltage; u w is the control signal of the wind electronic system, which is used to adjust the duty cycle δ of the DC / DC converter (in this special topology, u w =k tr / δ,k tr is the winding ratio in the transformer of the DC / DC converter); J is the moment of inertia of the wind wheel; L is the stator inductance; T t is the mechanical torque of the wind subsystem; P is the pole pair number of the wind turbine.

[0185] For the sake of simplicity, the above model can be expressed as follows:

[0186]

[0187] Among them, f w =[f w1 f w2 f w3 ] T and g w =[g w1 g w2 g w3 ] are all nonlinear vector functions, and their explicit forms are omitted for simplicity.

[0188] The mathematical model of the photovoltaic power generation system can be summarized as follows:

[0189]

[0190] where x s =[v pv i s ] T is the state vector of the photovoltaic power generation system; v pv is the terminal voltage of the photovoltaic array; i s The current injected into the DC bus of the photovoltaic power generation system; C and L are the capacitance and inductance of the DC / DC converter; u s is the control signal (switch control); i pv Represents the output current of the photovoltaic subsystem; n p Represents the number of photovoltaic panels connected in parallel in the photovoltaic array; n s Represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; I ph Represents the photocurrent under reference light intensity; I rs Represents the reverse saturation current of the photovoltaic cell; i pv and v pv are the output current and voltage of the photovoltaic panel respectively; q represents the electron charge constant; K represents the Boltzmann constant; T is the absolute temperature of the photovoltaic cell; A c is the bias coefficient of the PN junction (varies with the battery structure, probably between 1 and 5); R s is the series resistance.

[0191] The mathematical model of the photovoltaic power generation system can be summarized as follows:

[0192]

[0193] h s (x s )=0

[0194] Among them, f s =[f s1 fs2 ] T , g s =[g s1 g s2 ] T is a nonlinear vector function, h s (x s ) is a nonlinear scalar function, and its explicit form is omitted for brevity.

[0195] The mathematical model of the battery energy storage subsystem can be summarized as follows:

[0196]

[0197] Among them, v c The terminal voltage of the battery; i w is the output current of the wind power electronic system; i s The current injected into the DC bus for the photovoltaic power generation system; E b is the voltage source; R b is the equivalent series resistance of the battery; C b is the equivalent capacitance of the battery.

[0198] The sliding mode variable structure controller design module can design the first sliding mode variable structure controller and the second sliding mode variable structure controller for each subsystem under all working conditions (sufficient or insufficient wind power, sufficient or insufficient sunlight) based on the sliding mode control theory.

[0199] The expression of the first sliding mode variable structure controller is:

[0200]

[0201] in, and are the switching functions designed for the wind power subsystem when the wind power is sufficient and insufficient respectively; γ and ξ max is the designed constant factor.

[0202] The expression of the second sliding mode variable structure controller is:

[0203]

[0204] Among them, s s1 (x) = i L -i s and They are the switching functions designed for the photovoltaic power generation system when the wind is sufficient and insufficient.

[0205] The prediction controller design module designs a distributed economic model prediction controller at the supervisory layer by establishing an objective function that considers supply and demand balance and economic performance. A switching control working mechanism based on Lyapunov technology is introduced at the supervisory layer, that is, the feasible domain of the wind and solar subsystem under ideal and disturbed conditions is given offline, and the corresponding working mode is switched at the supervisory layer by online monitoring of changes in state variable values.

[0206] The distributed economic model prediction controller is:

[0207]

[0208] u i (τ)∈U i

[0209]

[0210] if V(x(t k ))≤ρ i '

[0211]

[0212] ifρ i ≥V i (x(t k ))>ρ i '

[0213] The switching control module is used to control the switching of the first sliding mode variable structure controller and the second sliding mode variable structure controller through a distributed economic model predictive controller, and output the optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem respectively.

[0214] In one embodiment of the present invention, see Fig.12, a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the distributed economic model predictive control method of the microgrid.

[0215] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the microgrid distributed economic model predictive control method in the above embodiment.

[0216] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0218] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0220] 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A microgrid distributed economic model predictive control method, characterized in that: The following steps are involved: Establish a mathematical model of the wind power generation subsystem according to the dynamic characteristics of the wind power generation subsystem, establish a mathematical model of the photovoltaic power generation subsystem according to the dynamic characteristics of the photovoltaic power generation subsystem, and establish a mathematical model of the battery energy storage subsystem according to the dynamic characteristics of the battery energy storage subsystem; Based on the sliding mode control theory under all working conditions, the first sliding mode variable structure controller is designed according to the mathematical model of the wind power generation subsystem; the second sliding mode variable structure controller is designed according to the mathematical model of the photovoltaic power generation subsystem and the mathematical model of the battery energy storage subsystem; Establish the economic objective function of the microgrid and design a distributed economic model predictive controller at the supervisory layer based on the economic objective function; The switching of the first sliding mode variable structure controller and the second sliding mode variable structure controller is controlled by a distributed economic model predictive controller, and the optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem are output respectively.

2. A microgrid distributed economic model predictive control method according to claim 1, characterized in that: The mathematical model of the wind power generation subsystem, photovoltaic power generation subsystem and battery energy storage subsystem is abbreviated as: Among them, f ii , g ii , f ij and g ij are all nonlinear vector functions, x i (t) is the state of the ith subsystem, x j (t) is the state of the jth subsystem, u i (t) is the input of the ith subsystem, u j (t) is the input of the jth subsystem.

3. A microgrid distributed economic model predictive control method according to claim 1, characterized in that: The step of designing a first sliding mode variable structure controller according to the mathematical model of the wind turbine subsystem specifically includes: The first sliding mode variable structure controller is designed so that when the wind energy is sufficient, the wind turbine subsystem tracks the given power; when the wind energy is insufficient, the wind turbine of the wind turbine subsystem operates at the maximum power.

4. A microgrid distributed economic model predictive control method according to claim 1, characterized in that: The step of designing a second sliding mode variable structure controller according to the mathematical model of the photovoltaic power generation electronic system specifically includes: The second sliding mode variable structure controller is designed so that when the sunlight is sufficient, the photovoltaic power generation subsystem tracks the given power; when the sunlight is insufficient, the photovoltaic power generation subsystem operates at the maximum power.

5. A microgrid distributed economic model predictive control method according to claim 1, characterized in that: The steps of establishing the economic objective function specifically include: Establish the microgrid supply and demand balance function, the specific expression is: l1=α(P t -P w -P s ) 2 Where l1 represents the microgrid supply and demand balance function; α is a constant coefficient; P t is the external load demand; P w is the real-time power generation of the wind subsystem; P s The real-time power generation of the photovoltaic subsystem; Establish a wind power generation function, the specific expression is: Among them, l2 is the wind power generation function; β is a constant coefficient; P s The real-time power generation of the photovoltaic subsystem; The mechanical loss function of the fan is established, and the specific expression is: l3=σΔT t Where l3 is the mechanical loss function of the fan; σ is a constant coefficient; T t is the mechanical torque of the fan; Establish the battery benefit cost function, the specific expression is: Where l4 is the battery benefit cost function; ε and η are constant coefficients; ΔP b is the changing power of the battery; Soc is the charge state of the battery; Establish the DC bus voltage function, the specific expression is: l5=δ(v b -v0) 2 Where l5 is the DC bus voltage function; δ is a constant coefficient; v b is the DC bus voltage; v0 is the DC bus voltage reference value; The microgrid supply and demand balance function, wind power generation function, wind turbine mechanical loss function, battery benefit cost function and DC bus voltage function are linearly weighted to finally obtain the economic objective function of the microgrid, which is specifically expressed as follows: L=R1l1+R2l2+R3l3+R4l4+R5l5 Among them, R1 is the weight factor of the microgrid supply and demand balance function; R2 is the weight factor of the wind power generation function; R3 is the weight factor of the wind turbine mechanical loss function; R4 is the weight factor of the battery benefit cost function; R5 is the weight factor of the DC bus voltage function.

6. A microgrid distributed economic model predictive control method according to claim 1, characterized in that: The expression of the distributed economic model prediction controller is: you i (τ)∈U i Model1: if V(x(t k ))≤ρ i ′ Model2: ifr i ≥V i (x(t k ))>r i ′ Among them, τ is the integral variable; ρ′ i Ideal feasible domain The radius of i ′ is the perturbation feasible region The radius of is the state measurement value of subsystem i at time k, and is the initial value of the state variable of subsystem i during the rolling optimization process; u i DEMPC The control sequence obtained by solving the optimization problem under the ideal model; is the constructed Lyapunov function; When the state variable is monitored to be in the ideal feasible region, ρ' When the system controller switches to the distributed economic model predictive control; when the state variable is detected to be in the ideal feasible domain Ω ρ' and the perturbation feasible region Ω ρ When the system control input is switched to Drive the system state into the ideal feasible domain Ω ρ' .

7. A microgrid distributed economic model predictive control method according to claim 1, characterized in that: Also includes: The optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem are respectively used as reference inputs of each subsystem controller to track the unit output of the subsystem.

8. A microgrid distributed economic model predictive control system, characterized in that: include: A mathematical model building module is used to build a mathematical model of a wind power generation subsystem according to the dynamic characteristics of the wind power generation subsystem, build a mathematical model of a photovoltaic power generation subsystem according to the dynamic characteristics of the photovoltaic power generation subsystem, and build a mathematical model of a battery energy storage subsystem according to the dynamic characteristics of the battery energy storage subsystem; The sliding mode variable structure controller design module is used to design the first sliding mode variable structure controller based on the sliding mode control theory and the mathematical model of the wind turbine subsystem under all working conditions; Design a second sliding mode variable structure controller based on the mathematical model of the photovoltaic power generation subsystem and the mathematical model of the battery energy storage subsystem; The prediction controller design module is used to establish the economic objective function of the microgrid and design the distributed economic model prediction controller at the supervision layer according to the economic objective function; The switching control module is used to control the switching of the first sliding mode variable structure controller and the second sliding mode variable structure controller through a distributed economic model prediction controller, and output the optimal operating power of the wind power generation subsystem, the optimal operating power of the photovoltaic power generation subsystem and the optimal operating power of the battery energy storage subsystem respectively.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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