Distributed precise time secondary control method and device for smart microgrid

Abstract

The present invention proposes a distributed precision time secondary control method and device for a smart microgrid, comprising: establishing a mathematical model of distributed generators in the smart microgrid; designing a distributed precision time secondary control algorithm; designing a communication strategy between distributed generators through event-triggered communication; combining the secondary control algorithm and the communication strategy to restore the output frequency and voltage of the distributed generators to rated values ​​while reducing communication costs; establishing a mathematical model for the power generation cost of the distributed generators, and designing an optimization algorithm to minimize their power generation costs. The present invention reduces the communication burden by designing a communication strategy between distributed generators; the proposed control algorithm can achieve precise frequency and voltage regulation within a manually set time, making the present invention more adaptable to demand; secondly, the power generation cost of the distributed generators is taken into account, and the total power generation cost is minimized through the algorithm, making the present invention more practical.

Description

Technical Field

[0001] The present invention relates to the field of smart microgrids, and in particular to a distributed precise time secondary control method and device for smart microgrids. Background Art

[0002] A smart microgrid is a small power generation system composed of distributed energy resources, energy storage devices, energy conversion devices, loads, and monitoring devices. Distributed secondary control is a typical method for regulating smart microgrids. It is a multidisciplinary technology that integrates fields such as electricity, information theory, and automatic control. With the rapid development of society, daily life, industrial development, and other activities are inseparable from electricity. At the same time, facing the current situation of energy depletion and environmental protection requirements, the vigorous development of renewable energy such as solar and wind power will inevitably become a major development trend in the future. As an effective solution for integrating intermittent and unstable renewable energy into the grid, smart microgrids can operate in both isolated and grid-connected modes. In isolated operation, they can rely on their own control to achieve power balancing control and power quality management.

[0003] However, smart microgrids are subject to various constraints in their actual applications, including frequency deviations caused by load fluctuations. In addition, primary control based on droop control will result in deviations between frequency and voltage and the rated values. Therefore, the most basic purpose of secondary control is to restore the deviations in frequency and voltage. In addition, distributed generators exchange information through communication networks, and continuous or periodic information exchange will cause problems such as excessive burden on the communication network and high communication costs. Therefore, designing a reasonable communication strategy has practical engineering significance for reducing the burden on the communication network. On the other hand, due to the different properties of each generator, its power generation cost is also different. By reasonably allocating the output power of the generator, the power generation cost can be greatly reduced, and the economic benefits of the entire microgrid can be greatly improved.

[0004] Therefore, considering the frequency and voltage regulation of distributed generators in smart microgrids, the communication burden and the total power generation cost of the system, it is a technical problem that needs to be solved urgently to regulate them by designing algorithms. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a distributed precise time quadratic control method for a smart microgrid, which mainly includes the following steps:

[0006] Establish the mathematical model of distributed generators and primary control model in smart microgrids, and establish the control error;

[0007] Design a distributed precise time quadratic control algorithm,

[0008] Design communication strategies between distributed generators through event-triggered communication;

[0009] Combining the distributed precise time quadratic control algorithm and communication strategy, the output frequency and voltage of the distributed generator can be restored to the rated value while reducing the communication cost, even if the control error approaches 0;

[0010] A mathematical model is established for the power generation cost of distributed generators, and an optimization algorithm is designed to minimize its power generation cost.

[0011] Furthermore, the mathematical model of the distributed generator is as follows:

[0012]

[0013] In formula 1, is the state vector; P i ,Q i are active and reactive power respectively; D i is a known external disturbance, δ i represents the angle of the reference frame of distributed generator i relative to the common reference frame; are the primary and secondary components of the auxiliary variable of the voltage controller; are the primary and secondary components of the auxiliary variable of the current controller; I Li ,v oi ,i oi The first and second components of i Li ,v oi ,i oi This is the quantity related to the LC filter and the output interface.

[0014] Furthermore, the primary control is based on traditional droop control, and its primary control model for distributed generator i is formula 2:

[0015]

[0016] In formula 2, w i is the angular frequency of distributed generator i, are the d-axis and q-axis voltages of generator i, respectively; are nominal setting values; P i ,Q i are the active and reactive powers of distributed generator i, respectively; is the control coefficient of droop control; terminal voltage

[0017] Taking the derivative of equation 2, we can get equation 3:

[0018]

[0019] In formula three, They are the secondary control signals of frequency and voltage respectively;

[0020] From formula 3, we can get Formula 4:

[0021]

[0022] In formula 4, Design the power control optimization algorithm; The secondary control signals are updated according to the designed secondary control signals. It is calculated by measuring the information of its own generator and the information of neighboring generators, r represents the integral variable, and t represents time;

[0023] The control error is constructed for distributed generator i (i=1, ..., n), that is, the difference between the output frequency and voltage of the distributed generator and the rated frequency and voltage, as shown in Equation 5:

[0024]

[0025] In formula five, are the control errors of frequency and voltage of generator i, w ref ,v ref These are the rated values ​​to which the frequency and voltage need to be restored respectively.

[0026] Furthermore, the design form of the distributed precise time quadratic control algorithm is as shown in Formula 6:

[0027]

[0028] In Equation 6, parameter β>0; μ is a time-varying function, and its specific form is Equation 7:

[0029]

[0030] In formula 7, parameter h>0, T s That is, the control time that can be set manually;

[0031] In formula 6 and The specific form is as follows:

[0032]

[0033] where a ij is the element in the i-th row and j-th column of the Laplace matrix L; use graph theory to describe the communication network between distributed generators; define the adjacency matrix A = [a ij ], where a ij>0 means that distributed generator j can receive information from i, otherwise, a ij =0; Laplace matrix L = [l ij ],in l ij =-a ij ,in, is the latest triggering moment of distributed generator i; is the frequency of the trigger moment; is the voltage at the triggering moment, and the subscript j represents the distributed generator j; if b i >0, then the distributed generator i can receive the reference signal w ref ,v ref Otherwise b i =0;N i Represents the set of neighboring generators of distributed generator i.

[0034] Furthermore, the communication strategy between distributed generators designed by event-triggered communication is as shown in Formula 9:

[0035]

[0036] In formula nine, Represents the latest triggering moment of generator i, is the next triggering moment; θ>0,τ i >0, The measurement error is defined as the difference between the state at the trigger moment and the real-time state, which can be expressed as Equation 10:

[0037]

[0038] Among them, w i (t), v i (t) is the real-time frequency and real-time voltage of distributed generator i;

[0039] Define auxiliary variables: ξ i =[w i (t),v i (t)],ξ=[ξ1,…,ξ N ] T , ζ ref =[w ref ,v ref ] T , α=[α1,…,α N ] T e=[e1,…,e N ] T , u=[u1,…,u N ] T ;

[0040] Based on the above definition, the measurement error equation 10 can be expressed in a compact form as equation 11:

[0041]

[0042] Furthermore, the distributed precise time quadratic control algorithm and communication strategy are combined to restore the output frequency and voltage of the distributed generator to the rated value while reducing the communication cost, specifically including:

[0043] According to the auxiliary variable ξ defined i =[w i ,v i ], the control error can be written as a compact form of vector representation, as shown in Equation 12:

[0044] σ i =ξ i -ζ ref Formula 12

[0045] Based on the measurement error formula (10) and the defined formula (12), the following equivalent expression (13) can be obtained:

[0046]

[0047] According to Equation 12 and Equation 13, the global error is defined as: σ=[σ1,…σ N ] T , According to the global error and Equation 8, Equation 14 can be obtained:

[0048]

[0049] Where L is the Laplace matrix and B is the diagonal matrix, which is defined as B=diag[b1,…,b n ], if b i >0, then the distributed generator i can receive the reference signal w ref ,v ref Otherwise b i = 0, distributed generator i cannot receive the reference signal w ref ,v ref , is the Kronecker product, I m is the m×m identity matrix.

[0050] According to the defined global error variable σ, Lyapunov theory is used to analyze the frequency and voltage recovery of the smart microgrid under the precise quadratic time control algorithm. Its V function is constructed as Equation 15:

[0051]

[0052] In Equation 15, Φ is a diagonal matrix, which is defined as: Diagonal elements: Matrix Q = L + B, 1 N is a column vector whose elements are all 1;

[0053] By taking the derivative of the constructed V function and combining it with the designed event trigger mechanism, we can get Equation 16:

[0054]

[0055] Among them, w1, w2, and w3 are related quantities obtained in the derivation process. According to the form of the V function, it can be seen that the V function is positive definite, that is, the output frequency and voltage of the distributed generator are restored to the rated frequency and voltage.

[0056] Furthermore, the mathematical model for the power generation cost of the distributed generator is established as Formula 17:

[0057]

[0058] In formula seventeen, is the cost coefficient of distributed generator i, P i is the active output power of generator i; C i (P i ) is the electricity generation cost of generator i;

[0059] The total power generation cost of all generators, that is, the optimization objective, can be expressed as Equation 18:

[0060]

[0061] In formula 18, P i (0) is the initial power of each generator, P D is the total load, n represents the number of distributed generators;

[0062] In order to minimize the power generation cost, the following equation must be established, as shown in equation 19:

[0063] η1(P1)=η2(P2)=…=η n (P n )=η * Formula 19

[0064] In formula 19, η* is the optimal incremental cost, for η i Taking the derivative we get The optimization algorithm to be designed.

[0065] Furthermore, by designing an optimization algorithm To minimize the total power generation cost of the smart microgrid, its specific expression is shown in Formula 20:

[0066]

[0067] in As shown in formula 21:

[0068]

[0069] Among them, P i (0) is the initial output power of distributed generator i, then

[0070] According to another aspect of the present invention, a distributed precise time secondary control device for a smart microgrid is provided, comprising the following modules:

[0071] A control error establishment module is used to establish a mathematical model of distributed generators and a primary control model in the smart microgrid, and to establish a control error;

[0072] Control algorithm design module, used to design distributed precise time quadratic control algorithm,

[0073] Communication strategy design module, used to design communication strategies between distributed generators through event-triggered communication;

[0074] The frequency and voltage control module is used to combine the distributed precise time quadratic control algorithm and communication strategy to restore the output frequency and voltage of the distributed generator to the rated value while reducing the communication cost, even if the control error approaches 0;

[0075] The power generation cost optimization module is used to establish a mathematical model for the power generation cost of distributed generators and design an optimization algorithm to minimize their power generation cost.

[0076] The beneficial effects brought about by the technical solution provided by the present invention are:

[0077] The present invention proposes a distributed precise time secondary control method and device for a smart microgrid, establishes a mathematical model of distributed generators and a primary control model in the smart microgrid, and establishes a control error; designs a distributed precise time secondary control algorithm; designs a communication strategy between distributed generators through event-triggered communication; combines the distributed precise time secondary control algorithm and the communication strategy to restore the output frequency and voltage of the distributed generators to the rated value while reducing the communication cost, even if the control error approaches 0; establishes a mathematical model for the power generation cost of the distributed generators, and designs an optimization algorithm to minimize their power generation cost. Based on the above technical solutions, the present invention is more engineering-significant, and reduces the communication burden by designing a communication strategy between distributed generators; the proposed controller can achieve precise frequency and voltage regulation within a manually set time, making the present invention more adaptable to demand; secondly, the power generation cost of the distributed generators is taken into account, and the power generation cost of the entire system is minimized through the algorithm, making the present invention more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0079] Figure 1 This is an execution flow chart of a distributed precise time secondary control method for a smart microgrid according to an embodiment of the present invention;

[0080] Figure 2 is a communication topology diagram between four distributed generators in an embodiment of the present invention;

[0081] Figure 3 is a control block diagram in an embodiment of the present invention, which reflects the designed frequency and voltage control algorithm formula 6, communication strategy formula 9, and the relationship between;

[0082] Figure 4 This is a voltage evolution diagram of the four generators under the designed control algorithm in the embodiment of the present invention within a set time of 3 seconds;

[0083] Figure 5 This is a frequency evolution diagram of four generators under the designed control algorithm in the embodiment of the present invention within a set time of 3 seconds;

[0084] Figure 6 It is the communication time of the four generators under the designed control algorithm in the embodiment of the present invention within the set time of 3s;

[0085] Figure 7 : This is a voltage evolution diagram of the four generators under the designed control algorithm in the embodiment of the present invention within a set time of 4 seconds;

[0086] Figure 8 This is a frequency evolution diagram of four generators under the designed control algorithm in the embodiment of the present invention within a set time of 4 seconds;

[0087] Figure 9 It is the communication time of the four generators under the designed control algorithm in the embodiment of the present invention within the set time of 4s;

[0088] Figure 10 It is the evolution result of the optimization algorithm designed in the embodiment of the present invention, reflecting the consistency of incremental cost, the output power of each generator and the total power generation cost;

[0089] Figure 11 It is a structural diagram of a distributed precise time secondary control device of a smart microgrid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0090] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0091] To further demonstrate the effectiveness of the proposed control method, the following simulation is used. This embodiment of the present invention provides a distributed precision time quadratic control method for a smart microgrid. This method considers the constraints of the smart microgrid's communication network and reduces the communication burden by designing an event-triggered communication strategy. Furthermore, a novel control algorithm is proposed to achieve precise frequency and voltage regulation in the smart microgrid. Finally, a mathematical model is established for the generator's power generation cost, which is then optimized to minimize the total power generation cost of the microgrid.

[0092] refer to Figure 1 The embodiment of the present invention provides a distributed precise time quadratic control method for a smart microgrid, which specifically includes the following contents:

[0093] S1: Establish mathematical model and primary control model for distributed generators of smart microgrid, and establish control error

[0094] In step S1, a mathematical model of a distributed generator is established, as shown in Formula 1:

[0095]

[0096] In the above formula, is the state vector; P i ,Q i are active and reactive power respectively; D i is a known external disturbance, δ i represents the angle of the reference frame of distributed generator i relative to the common reference frame; are the primary and secondary components of the auxiliary variable of the voltage controller; are the primary and secondary components of the auxiliary variable of the current controller; I Li ,v oi ,i oi The first and second components of i Li ,v oi ,i oi This is the quantity related to the LC filter and the output interface.

[0097] The primary control is based on traditional droop control, and its primary control model for distributed generator i is as shown in Equation 2:

[0098]

[0099] In the above formula, w i is the angular frequency of distributed generator i, are the d-axis and q-axis voltages of generator i, respectively; are nominal setting values; P i ,Q i are active and reactive power respectively; is the control coefficient of droop control; Equation 2 reflects the relationship between the frequency and voltage of each generator and the active and reactive power of the generator; among them, the terminal voltage Taking the derivative of equation 2, we can get equation 3:

[0100]

[0101] In formula three, They are the secondary control signals of frequency and voltage, which are the control algorithms to be designed; For the power control optimization algorithm to be designed, the optimization algorithm is designed according to the incremental cost to minimize the power generation cost; from formula 3, we can get Formula 4:

[0102]

[0103] They are updated in the secondary control stage respectively, and their forms are shown in Equation 4, where the secondary control signal The control error is calculated by measuring the information of the own generator and the information of the neighboring generators. The control error is constructed for the distributed generator i (i = 1, ..., n), that is, the difference between the output frequency and voltage of the distributed generator and the rated frequency and voltage, as shown in Equation 5:

[0104]

[0105] in, are the recovery errors of frequency and voltage respectively, w ref ,v ref are the values ​​to which the frequency and voltage need to be restored, respectively. The purpose of the designed controller is to make The two errors are reduced to the range that meets the requirements, and the following analysis will be conducted To verify the effectiveness of the designed control algorithm.

[0106] S2: Design a distributed precise time quadratic control algorithm;

[0107] The distributed precise time quadratic control algorithm is also called the precise time quadratic frequency and voltage control algorithm. Its design form is shown in Expression 6, which can achieve the frequency and voltage at the set time T s Always restore to rated voltage and frequency:

[0108]

[0109] In Equation 6, parameter β>0, μ is a time-varying function, and its specific form is as shown in Equation 7:

[0110]

[0111] In formula 7, parameter h>0, T s This is the control time that can be set manually. and The specific form is as follows:

[0112]

[0113] where a ij is the element in the i-th row and j-th column of the Laplace matrix L; graph theory is used here to describe the communication network between distributed generators; Figure 2 As shown, Figure 2 This is the communication topology diagram between the four distributed generators. Only distributed generator 1 can receive the reference signal. The Laplace matrix and B matrix can be obtained from the communication topology diagram:

[0114]

[0115] The simulation parameters are set as follows: β = 12, h = 1.5, θ = 0.9, τ1 = τ2 = τ3 = τ4 = 1.8, w ref =314.16rad / s,v ref =380V.

[0116] Define the adjacency matrix A = [a ij ], where a ij>0 means that distributed generator j can receive information from i, otherwise, a ij =0; Laplace matrix L = [l ij ],in l ij =-a ij ,in, is the latest triggering moment of distributed generator i; is the frequency of the trigger moment; is the voltage at the triggering moment, and the subscript j represents the distributed generator j; if b i >0, then the distributed generator i can receive the reference signal w ref ,v ref Otherwise b i =0;N i Represents the set of neighboring generators of distributed generator i.

[0117] Under the designed control algorithm, the voltage and frequency evolution results of each distributed generator are as follows: Figure 4 、 5 As shown in Figures 7 and 8, the simulation results show that the frequency and voltage can be restored at the set 3s and 4s, that is, the voltage of each generator is restored to 380V and the frequency is restored to 314rad / s.

[0118] S3: Design communication strategies between distributed generators via event-triggered communication;

[0119] Specifically, the communication strategy between distributed generators designed through event-triggered communication is as shown in Formula 9:

[0120]

[0121] in, Represents the latest triggering moment of generator i, is the next triggering moment; θ>0,τ i >0, When the designed event triggering strategy is met, distributed generator i will transmit its state information to neighboring generators, and update the controller at this moment to regulate its own state. This method can reduce unnecessary communication. The measurement error is defined as the difference between the state at the trigger moment and the real-time state, which is specifically expressed as Equation 10:

[0122]

[0123] Among them, w i (t), v i (t) is the real-time frequency and real-time voltage of distributed generator i.

[0124] Define auxiliary variables: ξ i =[w i ,v i ],ξ=[ξ1,…,ξ N ] T , ζ ref =[w ref ,v ref ] T , α=[α1,…,α N ] T , e=[e1,…,e N ] T , u=[u1,…,u N ] T .

[0125] Based on the above definition, the measurement error can be expressed in the following compact form as shown in Equation 11:

[0126]

[0127] refer to Figure 6 and Figure 9 , Figure 6 and Figure 9 It reflects the communication time and frequency of each generator under the designed communication strategy.

[0128] S4: Combine the distributed precise time quadratic control algorithm and communication strategy to restore the output frequency and voltage of the distributed generator to the rated value while reducing the communication cost, even if the control error approaches 0;

[0129] Furthermore, in order to achieve accurate time frequency and voltage recovery, according to the defined auxiliary variable ξ i =[w i ,v i ], the control error (Equation 5) can be written in a compact form of vector representation, as shown in Equation 12:

[0130] σ i =ξ i -ζ ref Formula 12

[0131] Based on the measurement error formula 11 and the defined formula 12, the following equivalent expression 13 can be obtained:

[0132]

[0133] According to Equation 12 and Equation 13, the global error is defined as: σ=[σ1,…σ N ] T , According to the global error and Equation 8, Equation 14 can be derived:

[0134]

[0135] Where L is the Laplace matrix and B is the diagonal matrix, which is defined as B=diag[b1,…,b n ], if b i >0, then the distributed generator i can receive the reference signal w ref ,v ref Otherwise b i = 0, distributed generator i cannot receive the reference signal w ref ,v ref , is the Kronecker product, I m is the m×m identity matrix.

[0136] According to the defined global error variable σ, Lyapunov theory is used to analyze the frequency and voltage recovery of the smart microgrid under the proposed precise time control algorithm. Its V function is constructed as Equation 15:

[0137]

[0138] In Equation 15, Φ is a diagonal matrix, which is defined as: Diagonal elements: Matrix Q = L + B, 1 N is a column vector whose elements are all 1; according to the form of the V function, the V function is positive definite. Taking the derivative of the constructed V function and combining it with the designed event trigger mechanism formula 9, we can get formula 16:

[0139]

[0140] Among them, w1, w2, and w3 are related quantities obtained during the derivation process.

[0141] The obtained V function is stable in agreed time, that is, the designed algorithm can realize precise time voltage and frequency regulation of the smart microgrid, and user-defined precise time regulation can be achieved simply by setting the adjustment time Ts.

[0142] Due to the geographical location of each distributed generator, there is a long distance between each generator, and the information of each generator is transmitted between the generators through the communication network. Real-time information exchange will bring a huge burden to the communication network. Therefore, the present invention considers adopting an event-triggered communication strategy to reduce the communication burden.

[0143] S5: Establish a mathematical model for the power generation cost of distributed generators and design an optimization algorithm to minimize the total power generation cost of the smart microgrid.

[0144] The economic operation of the smart microgrid is to minimize the total power generation cost while meeting the supply and demand balance. The mathematical model of the total power generation cost can be expressed as shown in Equation 17:

[0145]

[0146] In formula seventeen, is the cost coefficient of distributed generator i, P i is the active output power of generator i. The optimization objective can be expressed as Equation 18:

[0147]

[0148] In formula 18, P D is the total load, P i (0) is the initial output power of each generator. In order to minimize the total power generation cost and realize the economic operation of the smart microgrid, it is necessary to design an optimization algorithm to achieve the consistency of the incremental cost of all generators, that is, Formula 19:

[0149] η1(P1)=η2(P2)=…=η n (P n )=η * Formula 19

[0150] In formula 19, η * is the optimal incremental cost. i Taking the derivative we get The optimization algorithm to be designed.

[0151] By design The total power generation cost of the smart microgrid is minimized by achieving incremental cost consistency, which is specifically expressed as Equation 20:

[0152]

[0153] in The specific expression is formula 21:

[0154]

[0155] Since the power generation cost of each generator is related to its own properties and has a nonlinear relationship with the output power of the generator, this paper considers the different power generation cost coefficients of generators with different properties, models the power generation cost of the generator as a summation, and minimizes the power generation cost by designing an optimization algorithm. Figure 10 , Figure 10 is the evolution result under the designed optimization algorithm, which reflects the consistency of incremental cost, the output power of each generator and the total power generation cost. Figure 10 It can be seen that under the effect of the optimization algorithm designed in the present invention, the total cost of each generator gradually decreases and eventually remains at a stable value.

[0156] The relationship between primary control and the designed precise time secondary control algorithm, communication strategy and optimization algorithm can be found in Figure 3 , Figure 3 The control block diagram of distributed generator i (i = 1, ..., n) reflects the data sources of the control algorithm of generator i, such as reference signal, frequency signal, voltage signal, power signal of neighboring generators and frequency, voltage signal and power signal of its own generator. After adding, we get After adding, we get The communication strategy is combined with the ability to determine whether distributed generator i should exchange information with its neighboring generators. If the conditions are met, communication occurs; otherwise, no communication occurs. This communication strategy reduces communication costs. The control process for other distributed generators is the same as described above.

[0157] The following describes a distributed precise time secondary control device for a smart microgrid provided by the present invention. The distributed precise time secondary control device described below and the distributed precise time secondary control method described above can refer to each other.

[0158] like Figure 11 As shown, a distributed precise time secondary control device for a smart microgrid includes the following modules:

[0159] Control error establishment module 01 is used to establish a mathematical model of distributed generators and a primary control model in the smart microgrid, and to establish a control error;

[0160] Control algorithm design module 02, used to design distributed precise time quadratic control algorithm,

[0161] Communication strategy design module 03, used to design communication strategies between distributed generators through event-triggered communication;

[0162] Frequency and voltage control module 04 is used to combine the distributed precise time quadratic control algorithm and communication strategy to restore the output frequency and voltage of the distributed generator to the rated value while reducing the communication cost, even if the control error approaches 0;

[0163] The power generation cost optimization module 05 is used to establish a mathematical model for the power generation cost of distributed generators and design an optimization algorithm to minimize their power generation cost.

[0164] The beneficial effects brought about by the implementation of the present invention are:

[0165] 1. Considering the limited communication resources between devices in smart microgrids, it is more practical to reduce the burden of communication networks by using event-triggered communication strategies;

[0166] 2. The designed secondary control algorithm can enable the frequency and voltage of each generator to return to the rated value at the accurate set time;

[0167] 3. Taking the power generation cost of the generator into consideration, a mathematical model of the power generation cost is established, and its cost function is optimized to minimize the power generation cost of all generators, which is more engineering meaningful.

[0168] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0169] The serial numbers of the embodiments of the present invention are for descriptive purposes only and do not represent superiority or inferiority of the embodiments. In a unit claim that lists several means, several of these means may be embodied by the same item of hardware. The use of the terms first, second, and third, etc., does not denote any order and should be construed as identifiers.

[0170] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distributed precise time quadratic control method for a smart microgrid, characterized in that: The following steps are involved: Establish the mathematical model of distributed generators and primary control model in smart microgrids, and establish the control error; Design distributed precise time quadratic control algorithm; Design communication strategies between distributed generators through event-triggered communication; Combining the distributed precise time quadratic control algorithm and communication strategy, the output frequency and voltage of the distributed generator can be restored to the rated value while reducing the communication cost, even if the control error approaches 0; Establish a mathematical model for the power generation cost of distributed generators and design an optimization algorithm to minimize their power generation cost; The primary control is based on traditional droop control, and its primary control model for distributed generator i is formula 2: Formula 2 In formula 2, is the angular frequency of distributed generator i, are the d-axis and q-axis voltages of generator i, respectively; are the nominal set values ​​of generator i respectively; are the active and reactive powers of distributed generator i, respectively; is the control coefficient of droop control; terminal voltage ; Taking the derivative of equation 2, we can get equation 3: Formula 3 In formula three, They are the secondary control signals of frequency and voltage respectively; From formula 3, we can get Formula 4: Formula 4 In formula 4, Design the power control optimization algorithm; The secondary control signals are updated according to the designed secondary control signals. It is calculated by measuring the information of its own generator and the information of neighboring generators, r represents the integral variable, and t represents time; The design form of the distributed precise time quadratic control algorithm is as follows: Formula 6 In formula 6, the parameters ; is a time-varying function, and its specific form is Equation 7: Formula 7 In formula 7, the parameters , That is, the control time that can be set manually; In formula 6 and The specific form is as follows: Style 8 in is the Laplace matrix The element in row i and column j; Use graph theory to describe the communication network between distributed generators; define the adjacency matrix ,in, Indicates that distributed generator j can receive information from i, otherwise, ; Laplacian matrix ,in , ,in, is the latest triggering moment of distributed generator i; is the frequency of the trigger moment; is the voltage at the triggering moment, and the subscript j represents the distributed generator j; if , then the distributed generator i can receive the reference signal ;otherwise ; Represents the set of neighboring generators of distributed generator i.

2. The distributed precise time quadratic control method according to claim 1, characterized in that: The mathematical model of the distributed generator is as follows: Formula 1 In formula 1, is the state vector; are active and reactive power respectively; is a known external disturbance, Represents distributed generators The angle of the reference frame relative to the ordinary reference frame; are the primary and secondary components of the auxiliary variable of the voltage controller; are the primary and secondary components of the auxiliary variable of the current controller; They are The first and second components of This is the quantity related to the LC filter and the output interface.

3. The distributed precise time quadratic control method according to claim 1, characterized in that: For distributed generators i ( i =1,…, n ) constructs the control error, which is the difference between the output frequency and voltage of the distributed generator and the rated frequency and voltage, as shown in Equation 5: Formula 5 In formula five, Generator i The frequency and voltage control errors, These are the rated values ​​to which the frequency and voltage need to be restored respectively.

4. The distributed precise time quadratic control method according to claim 1, characterized in that: The communication strategy between distributed generators designed by event-triggered communication is shown in Formula 9: Formula 9 In formula nine, Represents the generator i The latest trigger moment, For the next trigger moment; , , , , >0, the measurement error is defined as the difference between the state at the trigger moment and the real-time state, specifically expressed as formula 10: Style 10 in, , For distributed generators i Real-time frequency and real-time voltage; Define auxiliary variables: , , , , , , , , , , , , , ; Based on the above definition, the measurement error equation 10 can be expressed in a compact form as equation 11: Formula 11.

5. The distributed precise time quadratic control method according to claim 4, characterized in that: The above-mentioned method combines the distributed precise time quadratic control algorithm and the communication strategy to restore the output frequency and voltage of the distributed generator to the rated value while reducing the communication cost, specifically including: According to the defined auxiliary variables , the control error can be written as a compact form of vector representation, as shown in Equation 12: Formula 12 Based on the measurement error formula (10) and the defined formula (12), the following equivalent expression (13) can be obtained: Style Thirteen The global error is defined according to Equation 12 and Equation 13: , According to the global error and Equation 8, we can get Equation 14: Formula 14 in, L is the Laplace matrix, B The matrix is ​​a diagonal matrix, which is defined as ,like , then the distributed generator i Reference signal can be received ;otherwise , distributed generators i Unable to receive reference signal , is the Kronecker product, for The identity matrix of According to the defined global error variable Lyapunov theory is used to analyze the frequency and voltage recovery of smart microgrids under the precise quadratic time control algorithm. V The function is constructed as formula 15: Formula 15 In formula 15, is a diagonal matrix, which is defined as: ; Diagonal elements: ;matrix , is a column vector whose elements are all 1; On the structure V By taking the derivative of the function and combining it with the designed event trigger mechanism, we can get Equation 16: Formula 16 in, For the relevant quantities obtained in the derivation process, according to V The form of the function is known, V The function is positive definite, which means that the output frequency and voltage of the distributed generator are restored to the rated frequency and voltage.

6. The distributed precise time quadratic control method according to claim 1, characterized in that: The mathematical model for the power generation cost of the distributed generator is as shown in Formula 17: Formula 17 In formula seventeen, For distributed generators i The cost coefficient, For generators i Active output power; For generators i the cost of electricity generation; The total power generation cost of all generators, that is, the optimization objective, can be expressed as Equation 18: Form 18 In formula 18, is the initial power of each generator, is the total load, n Indicates the number of distributed generators; In order to minimize the power generation cost, the following equation must be established, as shown in equation 19: Formula 19 In formula 19, , is the optimal incremental cost, Taking the derivative we get , The optimization algorithm to be designed.

7. The distributed precise time quadratic control method according to claim 6, characterized in that: By designing an optimization algorithm To minimize the total power generation cost of the smart microgrid, its specific expression is shown in Formula 20: Formula 20 in As shown in formula 21: Formula 21 in is the initial output power of distributed generator i, then 。 8. A distributed precise time secondary control device for a smart microgrid, characterized in that: Includes the following modules: A control error establishment module is used to establish a mathematical model of distributed generators and a primary control model in the smart microgrid, and to establish a control error; Control algorithm design module, used to design distributed precise time quadratic control algorithm, Communication strategy design module, used to design communication strategies between distributed generators through event-triggered communication; The frequency and voltage control module is used to combine the distributed precise time quadratic control algorithm and communication strategy to restore the output frequency and voltage of the distributed generator to the rated value while reducing the communication cost, even if the control error approaches 0; The power generation cost optimization module is used to establish a mathematical model for the power generation cost of distributed generators and design an optimization algorithm to minimize their power generation cost; The primary control is based on traditional droop control, and its primary control model for distributed generator i is formula 2: Formula 2 In formula 2, is the angular frequency of distributed generator i, are the d-axis and q-axis voltages of generator i, respectively; are the nominal set values ​​of generator i respectively; are the active and reactive powers of distributed generator i, respectively; is the control coefficient of droop control; terminal voltage ; Taking the derivative of equation 2, we can get equation 3: Formula 3 In formula three, They are the secondary control signals of frequency and voltage respectively; From formula 3, we can get Formula 4: Formula 4 In formula 4, Design the power control optimization algorithm; The secondary control signals are updated according to the designed secondary control signals. It is calculated by measuring the information of its own generator and the information of neighboring generators, r represents the integral variable, and t represents time; The design form of the distributed precise time quadratic control algorithm is as follows: Formula 6 In formula 6, the parameters ; is a time-varying function, and its specific form is Equation 7: Formula 7 In formula 7, the parameters , That is, the control time that can be set manually; In formula 6 and The specific form is as follows: Style 8 in is the Laplace matrix The element in row i and column j; Use graph theory to describe the communication network between distributed generators; define the adjacency matrix ,in, Indicates that distributed generator j can receive information from i, otherwise, ; Laplacian matrix ,in , ,in, is the latest triggering moment of distributed generator i; is the frequency of the trigger moment; is the voltage at the triggering moment, and the subscript j represents the distributed generator j; if , then the distributed generator i can receive the reference signal ;otherwise ; Represents the set of neighboring generators of distributed generator i.

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