Micro-grid dynamic voltage control method and system based on model predictive control

Abstract

The invention discloses a micro-grid dynamic voltage control method and system based on model predictive control, and the method comprises the steps: collecting the real-time state data of each node of a micro-grid, and predicting the future state change of the micro-grid based on the real-time state data value; constructing a multi-objective optimization model, and solving the multi-objective optimization model in combination with the prediction result to obtain an optimal control vector; constructing a voltage control linear discretization state space model, and converting the model into a quadratic programming problem for solving to obtain an optimal reactive power regulation instruction of each adjustable reactive power resource of the microgrid; and controlling and adjusting the micro-grid based on the optimal control vector and the optimal reactive power adjustment instruction, monitoring the state of the micro-grid in real time, and dynamically adjusting the weight coefficient of the multi-target optimization model to optimize the energy efficiency of the micro-grid. The real-time performance and the energy efficiency of the dynamic voltage control strategy of the micro-grid can be remarkably improved, the uncertainty of load and power generation can be effectively handled, and the stability and the economical efficiency of micro-grid operation are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid control, and in particular to a microgrid dynamic voltage control method and system based on model predictive control. Background Art

[0002] Microgrids are small-scale, decentralized power systems that can operate in isolated mode or in parallel with the main grid. Due to the uncertainty of loads and distributed generation sources, voltage control in microgrids is a great challenge. Existing voltage control strategies based on model predictive control have problems such as large computational complexity, poor real-time performance, and insufficient energy efficiency optimization, and are in urgent need of improvement and optimization.

[0003] The microgrid dynamic voltage control methods in the prior art usually rely on experience or simple feedback control strategies, which are difficult to adapt to the dynamic changes in the operation of the microgrid, especially when there are large fluctuations in load and renewable energy generation. The prior art has at least the following technical problems:

[0004] 1. Long response time: Traditional voltage regulation methods often rely on discrete sampling and control cycles, resulting in a slow system response to sudden load changes or fluctuations in renewable energy generation.

[0005] 2. Low control accuracy: Traditional methods lack the ability to predict future states, which may lead to inaccurate control results. Especially under the complex multi-objective optimization and dynamic constraints in microgrids, the control effect is difficult to guarantee.

[0006] 3. Unable to effectively handle multi-objective optimization: The voltage control of the microgrid must not only meet the voltage stability requirements, but also optimize the regulation of reactive power, reduce energy consumption and improve system economy. Traditional methods are difficult to take these goals into account at the same time. Summary of the invention

[0007] In order to solve the deficiencies in the prior art, the present invention provides a microgrid dynamic voltage control method based on model predictive control, which can solve the technical problems of poor real-time performance and insufficient energy efficiency optimization of voltage control strategies in the prior art.

[0008] The present invention adopts the following technical solution.

[0009] A microgrid dynamic voltage control method based on model predictive control includes the following steps:

[0010] Collect the real-time status data of each node in the microgrid, and predict the future status changes of the microgrid based on the real-time status data value;

[0011] Construct a multi-objective optimization model, solve the multi-objective optimization model based on the prediction results, and obtain the optimal control vector;

[0012] A linear discretized state space model of voltage control is constructed and converted into a quadratic programming problem for solution to obtain the optimal reactive power regulation instructions for each adjustable reactive power resource in the microgrid.

[0013] The microgrid is controlled and regulated based on the optimal control vector and the optimal reactive power regulation instruction, the state of the microgrid is monitored in real time, and the weight coefficients of the multi-objective optimization model are dynamically adjusted to optimize the energy efficiency of the microgrid.

[0014] Preferably, the status data includes: voltage, current, active power, reactive power and phase angle of the microgrid.

[0015] Preferably, the multi-objective optimization model K is as follows:

[0016]

[0017] Where V t is the voltage vector of the microgrid at time t; V ref is the reference voltage vector; E t is the energy loss of the microgrid at time t; u t is the control vector of the microgrid at time t; α, β, γ represent the weight coefficients of voltage stability, energy efficiency optimization and control energy, respectively; N is the predicted time domain length.

[0018] Preferably, solving the multi-objective optimization model in combination with the prediction results includes using an MPC algorithm to solve the optimization problem, specifically including:

[0019] Set the initial control vector and initial weight coefficients α, β, γ, where α, β, γ represent the weight coefficients of voltage stability, energy efficiency optimization, and control energy, respectively;

[0020] The distributed gradient descent method is used to solve the optimization problem and obtain the optimal control vector;

[0021]

[0022] In the formula, u t is the optimal control vector.

[0023] Preferably, a voltage control linear discretization state space model is constructed, and the model is converted into a quadratic programming problem for solution, specifically including:

[0024] Construct the overall voltage control state space equations of the microgrid;

[0025] The overall voltage control state space equation of the microgrid is discretized and the constraint output equation is added to obtain the voltage control linear discretization state space model of the microgrid;

[0026] Considering the operating characteristics of photovoltaics, energy storage and SVG, the constraints for model solution are established;

[0027] A performance index function is constructed, and the linear discretized state space model of voltage control is transformed into a quadratic programming problem and solved under constraints to obtain the optimal reactive power regulation instructions for each adjustable reactive power resource in the microgrid.

[0028] Preferably, the overall voltage control state space equation is as follows:

[0029]

[0030] In the formula, x u (t) is the system state variable matrix in voltage control, u u (t) is the system control variable matrix, w u (t) is the system disturbance variable matrix, y u (t) is the system output variable matrix, A u , B u 、E u , C u is the parameter matrix. Its expressions are:

[0031]

[0032] Where n c，Q is the number of adjustable reactive sources in the microgrid, ΔU is the voltage change, ΔQ is the reactive change, JUQ is the reactive voltage sensitivity, I is the unit matrix, is the preset coefficient, is the disturbance function of the associated power change.

[0033] Preferably, the voltage control linear discretization state space model of the microgrid is as follows:

[0034]

[0035] Where: k and (k+1) are time points, x u (k+1) and x u (k) are the microgrid state variable matrices at time k+1 and time k, respectively, u u (k) is the system control input variable matrix at time k, ω u (k) is the matrix of external disturbance variables that can be measured at time k, y c,u (k) is the control output variable matrix at time k, y b，u (k) is the constraint output variable matrix at time k, A′ u , B′ u , E′ u , C c，u , C b，uThey are the discretized state transfer matrix, input matrix, error matrix, control output variable coefficient matrix and constraint output variable coefficient matrix respectively.

[0036] Preferably, the constraints for solving the model specifically include:

[0037] Taking into account the reactive power output constraint of adjustable photovoltaic in the dynamic voltage regulation process, its adjustable range can be expressed as:

[0038]

[0039] In the formula, Q pv Represents the reactive power output by photovoltaic power. For maximum reactive power output;

[0040] Considering the reactive output constraints of energy storage, reactive power can be adjusted bidirectionally and the adjustment range is as follows:

[0041]

[0042] In the formula, Q bess Represents the reactive power output by the energy storage, For maximum reactive power output;

[0043] Considering the upper and lower limits of SVG reactive power regulation, the reactive power output by SVG can be adjusted bidirectionally, and the adjustment range is as follows:

[0044]

[0045] In the formula, are the upper and lower limits of SVG reactive output, Q svg It is SVG reactive output;

[0046] The output constraints of the system are set based on the voltage control target, as follows:

[0047] ΔU min ≤ΔU i ≤ΔU max

[0048] Where: ΔU i is the voltage offset of microgrid node i relative to the reference value; ΔU max , ΔU min are the upper and lower limits of the voltage of microgrid node i respectively.

[0049] Preferably, the performance indicator function J is as follows:

[0050]

[0051] Where R and G are weighted matrices; ΔU represents the offset of the voltage relative to the reference value; and u represents the input variable matrix.

[0052] Preferably, the voltage control linear discretization state space model is transformed into a quadratic programming problem as shown in the following formula:

[0053] The voltage control linear discretization state space model is transformed into a quadratic programming problem as shown below:

[0054]

[0055] Where: C u , b(k+1|k) represents the constraint matrix, and its expressions are as follows:

[0056]

[0057] Where T and L are constraint matrices, Δu u，max (k), Δu u,min (k) are the upper and lower limits of reactive power change of each controlled reactive power regulation device at the kth sampling moment, u u,max (k),u u,min (k) are respectively the upper and lower limit constraints of the reactive output of each controlled reactive regulation device at the kth sampling moment;

[0058] By solving the above quadratic programming problem, the optimal reactive power regulation instructions of each adjustable reactive power resource are obtained, and the dynamic recovery control of the internal voltage of the microgrid system is realized.

[0059] Preferably, the dynamically adjusting weight coefficients of the multi-objective optimization model to optimize the microgrid energy efficiency specifically includes:

[0060] During the peak load period, the weight coefficients β and γ of energy efficiency optimization and energy control are increased; during the low load period, the weight coefficient α of voltage stability is increased.

[0061] The present invention also proposes a microgrid dynamic voltage control system based on model predictive control, which is used to implement the microgrid dynamic voltage control method based on model predictive control, including: a data acquisition and processing module, a control vector acquisition module, a reactive power regulation instruction acquisition module, and a control regulation module;

[0062] The data acquisition and processing module is used to collect the real-time status data of each node of the microgrid and predict the future status changes of the microgrid based on the real-time status data value;

[0063] The control vector acquisition module is used to construct a multi-objective optimization model, solve the multi-objective optimization model in combination with the prediction results, and obtain the optimal control vector;

[0064] The reactive power regulation instruction acquisition module is used to construct a linear discretization state space model of voltage control, and convert the model into a quadratic programming problem for solution to obtain the optimal reactive power regulation instructions for each adjustable reactive resource in the microgrid;

[0065] The control and regulation module controls and regulates the microgrid based on the optimal control vector and the optimal reactive power regulation instruction, monitors the state of the microgrid in real time, and dynamically adjusts the weight coefficients of the multi-objective optimization model to optimize the energy efficiency of the microgrid.

[0066] The present invention also provides a terminal, comprising a processor and a storage medium;

[0067] The storage medium is used to store instructions;

[0068] The processor is used to operate according to the instructions to execute the steps of the microgrid dynamic voltage control method based on model predictive control.

[0069] The present invention also proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the microgrid dynamic voltage control method based on model predictive control are implemented.

[0070] The beneficial effect of the present invention is that, compared with the prior art, the present invention can significantly improve the real-time performance and energy efficiency of the dynamic voltage control strategy of the microgrid based on MPC, effectively deal with the uncertainty of load and power generation, ensure the stability and economy of the operation of the microgrid, and significantly improve the real-time performance and energy efficiency of the system by introducing the fast-solving MPC algorithm and energy efficiency optimization strategy. The present invention obtains the optimal reactive power regulation instructions for each adjustable reactive power resource by designing a constraint model predictive controller and converting it into a general quadratic programming problem for solution, thereby realizing the rapid dynamic recovery control of the internal voltage of the microgrid system. The present invention has at least the following beneficial effects:

[0071] 1. Improve voltage control accuracy: By adopting a multi-objective optimization method based on model predictive control, the present invention can accurately predict the future load and power generation of the microgrid, and dynamically adjust the reactive power regulation instructions according to the predicted information, thereby effectively improving the voltage control accuracy.

[0072] 2. Enhance the system's dynamic response capability: By introducing state prediction and real-time optimization, the present invention can respond in advance to load fluctuations and changes in renewable energy power generation, reduce the impact of system fluctuations, and improve the system's response speed when facing dynamic changes.

[0073] 3. Optimize multi-objective balance: The present invention can comprehensively consider multiple objectives, including voltage stability, reactive power regulation and economy, and solve them through multi-objective optimization model and MPC algorithm to ensure that the microgrid can improve the utilization efficiency of reactive power and reduce energy loss while meeting the voltage control requirements.

[0074] 4. Realize optimal control under constraints: Through the constraint controller based on the state space model, the present invention can fully consider various constraints of the system when processing voltage regulation, such as equipment capacity, power generation limit, etc., to ensure that the optimization result is practical and feasible under the premise of satisfying the constraints.

[0075] 5. Improve the stability and safety of microgrids: The present invention can effectively deal with voltage fluctuations and uncertainties in microgrids, ensure long-term stable operation of the system, and improve the reliability and safety of power supply, especially when power demand fluctuates greatly or renewable energy generation is unstable. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of a microgrid dynamic voltage control method based on model predictive control in the present invention;

[0077] Figure 2 It is a structural diagram of the microgrid dynamic voltage control system based on model predictive control in the present invention. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0079] Reference Figure 1 , a microgrid dynamic voltage control method based on model predictive control is provided, comprising the following steps:

[0080] Step 1: Collect the real-time status data of each node in the microgrid, and predict the future state changes of the microgrid based on the real-time status data value;

[0081] Specifically, data collection collects real-time status data of each node in the microgrid, including: voltage, current, active power, reactive power, and phase angle of the microgrid; and predicts the state change of the microgrid based on the collected real-time data values, including predicting the changes in voltage, current, active power, reactive power, and phase angle of the microgrid when the grid is in the future time domain N.

[0082] Step 2: Construct a multi-objective optimization model, use the fast-solving MPC algorithm combined with the multi-objective optimization model to solve the optimization problem, and obtain the optimal control strategy;

[0083] Among them, the multi-objective optimization model K is as follows:

[0084]

[0085] Where Vt is the voltage vector of the microgrid at time t; Vre f is the reference voltage vector; E t is the energy loss of the microgrid at time t; u t is the control vector of the microgrid at time t; α, β, and γ represent the weight coefficients of voltage stability, energy efficiency optimization, and control energy, respectively; N is the length of the prediction time domain;

[0086] Furthermore, the fast-solving MPC algorithm uses a distributed gradient descent method to solve the multi-objective optimization model:

[0087] Initialization: Set the initial control vector and initial weight coefficients α, β, γ;

[0088] The distributed gradient descent method is used to solve the optimization problem and obtain the optimal control strategy u t ;

[0089]

[0090] Step 3: construct a constraint model predictive controller based on the state space model, and convert it into a quadratic programming problem to solve it, so as to obtain the optimal reactive power regulation instructions of each adjustable reactive power resource;

[0091] Specifically, step 3 includes:

[0092] Step 3-1, construct the microgrid overall voltage control state space equation:

[0093]

[0094] Where: x u (t) is the system state variable matrix in voltage control, u u (t) is the system control variable matrix, w u (t) is the system disturbance variable matrix, y u (t) is the system output variable matrix, A u , B u 、E u , C u is the parameter matrix. Its expressions are:

[0095]

[0096]

[0097] Where n c，Q is the number of adjustable reactive sources in the microgrid, ΔU is the voltage change, ΔQ is the reactive change, JUQ is the reactive voltage sensitivity, I is the unit matrix, and a and b are coefficients.

[0098] Step 3-2, discretize the overall voltage control state space equation of the microgrid and add the constraint output equation to obtain the voltage control linear discretization state space model of the microgrid system as follows:

[0099]

[0100] Where: k and (k+1) are time points, x u (k+1) and x u (k) are the microgrid state variable matrices at time k+1 and time k, respectively, u u (k) is the system control input variable matrix at time k, ω u (k) is the matrix of external disturbance variables that can be measured at time k, y c,u (k) is the control output variable matrix at time k, y b，u (k) is the constraint output variable matrix at time k, A′ u , B′ u , E′ u , C c，u , C b，u They are the discretized state transfer matrix, input matrix, error matrix, control output variable coefficient matrix and constraint output variable coefficient matrix respectively.

[0101] According to the principle of model predictive control method, based on the constructed voltage control linear discretization state space model, the current system state is used to predict the future dynamic changes of the system. The specific method is as follows:

[0102] Assume that the prediction horizon N of the MPC controller is p and control time domain N c Satisfy N p >N c , at the current k moment, with x u (k) As the starting point for predicting future changes in the system, the future state of the system can be expressed as follows based on the voltage control linear discretization state space model:

[0103] Y p (k+1|k)=S x x u (k)+S u u u (k)+S ωω u (k)+S y y u (k)

[0104] In the formula, parameter Y p , S x , S ω , S y , S u Respectively meet:

[0105]

[0106] Where n yc is the control output variable matrix y c，u The number of elements in (k).

[0107] Step 3-3, considering the operating characteristics of photovoltaic, energy storage and SVG, construct the control constraints for the constraint model predictive controller to optimize the solution;

[0108] When designing a voltage controller, it is necessary to fully consider the operating characteristics of photovoltaics, energy storage and SVG, and determine their reactive output limits as control constraints for optimization solutions, including:

[0109] Considering the reactive power output constraints of adjustable photovoltaics during dynamic voltage regulation:

[0110] Its adjustable range can be expressed as:

[0111]

[0112] In the formula, Q pv Represents the reactive power output by photovoltaic power. For maximum reactive power output;

[0113] Considering the reactive output constraint of energy storage:

[0114] Reactive power can be adjusted bidirectionally and the adjustment range is:

[0115]

[0116] In the formula, Qbess represents the reactive power output by the energy storage, For maximum reactive power output.

[0117] Consider the upper and lower limits of SVG reactive power regulation: the reactive power output by SVG can be adjusted in both directions, and the adjustment range is as follows:

[0118]

[0119] In the formula, is the upper and lower limits of SVG reactive output, Q svg Reactive output for SVG.

[0120] The output constraints of the system based on the voltage control target are as follows:

[0121] ΔU min ≤ΔU i ≤ΔU max

[0122] Where: ΔU i is the offset of the voltage at system node i relative to the reference value; ΔU max , ΔU min Its upper and lower limits.

[0123] Step 3-4, construct a performance index function, transform the constraint model predictive controller into a quadratic programming problem for solution, and obtain the optimal reactive power regulation instruction for each adjustable reactive power resource;

[0124] The performance index function J of the microgrid at time k is defined as:

[0125]

[0126] Where R and G are weighted matrices; ΔU represents the offset of the voltage relative to the reference value; and u represents the input variable matrix.

[0127] Furthermore, due to the existence of constraints, the analytical solution to the above optimization problem is difficult to obtain directly. It is necessary to first transform the constraint problem into a quadratic programming problem and then solve it using a numerical method. Specifically:

[0128] First, the system control constraints and control increment constraints are converted into the form of Cz ≥ b, that is, the reactive output constraints of controllable photovoltaic, energy storage and SVG are converted into:

[0129]

[0130] Where: Δu u，max (k), Δu u，min (k) are the upper and lower limits of reactive power change of each controlled reactive power regulation device at the kth sampling moment, u u，max (k) and u u,min (k) are the upper and lower limit constraints of reactive output of each controlled reactive regulation device at the kth sampling moment, T and L are constraint matrices, and their expressions are:

[0131]

[0132] Where: n u is the number of control quantities.

[0133] The output constraint of the system, i.e., the voltage deviation constraint, is converted into the form of Cz ≥ b:

[0134]

[0135] The detailed expressions of each variable in the formula are as follows:

[0136]

[0137] Where: n yb Output variable matrix y for constraints b，u The number of elements in (k).

[0138] Based on the above calculation formula, the optimization problem of constrained MPC is transformed into a general quadratic programming (QP) problem as shown below:

[0139]

[0140] Where: C u , b(k+1|k) represents the constraint matrix, and its expressions are as follows:

[0141]

[0142] By solving the above QP problem, the optimal reactive power regulation command of each adjustable reactive power resource can be obtained, thereby realizing the rapid dynamic recovery control of the internal voltage of the microgrid system.

[0143] Step 4: The control command obtained in step 2 and the optimal reactive power regulation command obtained in step 3 are respectively sent to the microgrid, and the energy efficiency is optimized according to the weight coefficient of the real-time load and power generation of the microgrid.

[0144] Specifically, step 4 also includes:

[0145] Instruction issuance: The control instructions and optimal reactive power regulation instructions are issued to each control unit of the microgrid for execution;

[0146] Dynamic adjustment: monitor load and power generation conditions in real time, dynamically adjust weight coefficients, and optimize energy efficiency.

[0147] Among them, the load and power generation conditions are monitored in real time, and the weight coefficients α, β, and γ are adjusted dynamically to optimize energy efficiency. Specifically: during peak load periods, the energy efficiency optimization weights β and γ are increased; during low load periods, the voltage stability weight α is increased.

[0148] Gradient update: Update the optimal control strategy based on the optimization results

[0149]

[0150] in, They represent the control strategies at time k+1 and time k respectively; η represents the learning rate.

[0151] like Figure 2 As shown, the present invention also proposes a microgrid dynamic voltage control system based on model predictive control, which is used to implement the above-mentioned microgrid dynamic voltage control method based on model predictive control. The system includes: a data acquisition and processing module, a control vector acquisition module, a reactive power regulation instruction acquisition module, and a control regulation module;

[0152] The data acquisition and processing module is used to collect the real-time status data of each node of the microgrid and predict the future status changes of the microgrid based on the real-time status data value;

[0153] The control vector acquisition module is used to construct a multi-objective optimization model, solve the multi-objective optimization model in combination with the prediction results, and obtain the optimal control vector;

[0154] The reactive power regulation instruction acquisition module is used to construct a linear discretization state space model of voltage control, and convert the model into a quadratic programming problem for solution to obtain the optimal reactive power regulation instruction for each adjustable reactive resource of the microgrid;

[0155] The control and regulation module controls and regulates the microgrid based on the optimal control vector and the optimal reactive power regulation instruction, monitors the state of the microgrid in real time, and dynamically adjusts the weight coefficients of the multi-objective optimization model to optimize the energy efficiency of the microgrid.

[0156] The beneficial effect of the present invention is that, compared with the prior art, the present invention can significantly improve the real-time and energy efficiency of the microgrid dynamic voltage control strategy, effectively cope with the uncertainty of load and power generation, and ensure the stability and economy of microgrid operation.

[0157] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0158] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive lists) of computer readable storage medium include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), static random access memories (SRAM), portable compact disk read-only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or convex structures in grooves on which instructions are stored, and any suitable combination thereof. Computer readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (e.g., light pulses by optical fiber cables), or electrical signals transmitted by wires.

[0159] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0160] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages-such as Smalltalk, C++, etc., and conventional procedural programming languages-such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network-including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0161] 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, and any modifications or equivalent replacements that do 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 dynamic voltage control method based on model predictive control, characterized in that: The following steps are involved: Collect the real-time status data of each node in the microgrid, and predict the future status changes of the microgrid based on the real-time status data value; Construct a multi-objective optimization model, solve the multi-objective optimization model based on the prediction results, and obtain the optimal control vector; A linear discretized state space model of voltage control is constructed and converted into a quadratic programming problem for solution to obtain the optimal reactive power regulation instructions for each adjustable reactive power resource in the microgrid. The microgrid is controlled and regulated based on the optimal control vector and the optimal reactive power regulation instruction, the state of the microgrid is monitored in real time, and the weight coefficients of the multi-objective optimization model are dynamically adjusted to optimize the energy efficiency of the microgrid.

2. The microgrid dynamic voltage control method based on model predictive control according to claim 1 is characterized in that: The state data includes: voltage, current, active power, reactive power, and phase angle of the microgrid.

3. The microgrid dynamic voltage control method based on model predictive control according to claim 1, characterized in that: The multi-objective optimization model K is as follows: Where Vt is the voltage vector of the microgrid at time t; Vre f is the reference voltage vector; E t is the energy loss of the microgrid at time t; u t is the control vector of the microgrid at time t; α, β, γ represent the weight coefficients of voltage stability, energy efficiency optimization and control energy, respectively; N is the predicted time domain length.

4. The microgrid dynamic voltage control method based on model predictive control according to claim 3 is characterized in that: Solving the multi-objective optimization model in combination with the prediction results includes using the MPC algorithm to solve the optimization problem, specifically including: Set the initial control vector and initial weight coefficients α, β, γ, where α, β, γ represent the weight coefficients of voltage stability, energy efficiency optimization, and control energy, respectively; The distributed gradient descent method is used to solve the optimization problem and obtain the optimal control vector; In the formula, u t is the optimal control vector.

5. The microgrid dynamic voltage control method based on model predictive control according to claim 1, characterized in that: Construct a linear discretized state space model for voltage control and convert the model into a quadratic programming problem for solution, including: Construct the overall voltage control state space equations of the microgrid; The overall voltage control state space equation of the microgrid is discretized and the constraint output equation is added to obtain the voltage control linear discretization state space model of the microgrid; Considering the operating characteristics of photovoltaics, energy storage and SVG, the constraints for model solution are established; A performance index function is constructed, and the linear discretized state space model of voltage control is transformed into a quadratic programming problem and solved under constraints to obtain the optimal reactive power regulation instructions for each adjustable reactive power resource in the microgrid.

6. The microgrid dynamic voltage control method based on model predictive control according to claim 5 is characterized in that: The overall voltage control state space equation is as follows: In the formula, x u (t) is the system state variable matrix in voltage control, u u (t) is the system control variable matrix, w u (t) is the system disturbance variable matrix, y u (t) is the system output variable matrix, A u , B u 、E u , C u is the parameter matrix. Its expressions are: Where n c，Q is the number of adjustable reactive sources in the microgrid, ΔU is the voltage change, ΔQ is the reactive change, JUQ is the reactive voltage sensitivity, I is the unit matrix, is the preset coefficient, is the disturbance function of the associated power change.

7. The microgrid dynamic voltage control method based on model predictive control according to claim 6, characterized in that: The voltage control linear discretization state space model of the microgrid is as follows: Where: k and (k+1) are time points, x u (k+1) and x u (k) are the microgrid state variable matrices at time k+1 and time k, respectively, u u (k) is the system control input variable matrix at time k, ω u (k) is the matrix of external disturbance variables that can be measured at time k, y c，u (k) is the control output variable matrix at time k, y b，u (k) is the constraint output variable matrix at time k, A′ u , B′ u , E′ u , C c，u , C b，u They are the discretized state transfer matrix, input matrix, error matrix, control output variable coefficient matrix and constraint output variable coefficient matrix respectively.

8. The microgrid dynamic voltage control method based on model predictive control according to claim 6, characterized in that: The constraints for solving the model specifically include: Taking into account the reactive power output constraint of adjustable photovoltaic in the dynamic voltage regulation process, its adjustable range can be expressed as: In the formula, Q pv Represents the reactive power output by photovoltaic power. For maximum reactive power output; Considering the reactive output constraints of energy storage, reactive power can be adjusted bidirectionally and the adjustment range is as follows: In the formula, Q bess Represents the reactive power output by the energy storage, For maximum reactive power output; Considering the upper and lower limits of SVG reactive power regulation, the reactive power output by SVG can be adjusted bidirectionally, and the adjustment range is as follows: In the formula, are the upper and lower limits of SVG reactive output, Q svg It is SVG reactive output; The output constraints of the system are set based on the voltage control target, as follows: ΔU min ≤ΔU i ≤ΔU max Where: ΔU i is the voltage offset of microgrid node i relative to the reference value; ΔU max , ΔU min are the upper and lower limits of the voltage of microgrid node i respectively.

9. The microgrid dynamic voltage control method based on model predictive control according to claim 6, characterized in that: The performance indicator function J is as follows: Where R and G are weighted matrices; ΔU represents the offset of the voltage relative to the reference value; and u represents the input variable matrix.

10. The microgrid dynamic voltage control method based on model predictive control according to claim 7, characterized in that: The voltage control linear discretization state space model is transformed into a quadratic programming problem as shown in the following formula: The voltage control linear discretization state space model is transformed into a quadratic programming problem as shown below: Where: C u , b(k+1|k) represents the constraint matrix, and its expressions are as follows: Where T and L are constraint matrices, Δu u，max (k), Δu u，min (k) are the upper and lower limits of reactive power change of each controlled reactive power regulation device at the kth sampling moment, u u，max (k) and u u，min (k) are respectively the upper and lower limit constraints of the reactive output of each controlled reactive regulation device at the kth sampling moment; By solving the above quadratic programming problem, the optimal reactive power regulation instructions of each adjustable reactive power resource are obtained, and the dynamic recovery control of the internal voltage of the microgrid system is realized.

11. The microgrid dynamic voltage control method based on model predictive control according to claim 1, characterized in that: The dynamic adjustment of the weight coefficients of the multi-objective optimization model to optimize the microgrid energy efficiency specifically includes: During the peak load period, the weight coefficients β and γ of energy efficiency optimization and energy control are increased; during the low load period, the weight coefficient α of voltage stability is increased.

12. A microgrid dynamic voltage control system based on model predictive control, used to implement the microgrid dynamic voltage control method based on model predictive control as described in any one of claims 1 to 11, characterized in that: include: Data acquisition and processing module, control vector acquisition module, reactive power regulation instruction acquisition module, control regulation module; The data acquisition and processing module is used to collect the real-time status data of each node of the microgrid and predict the future status changes of the microgrid based on the real-time status data value; The control vector acquisition module is used to construct a multi-objective optimization model, solve the multi-objective optimization model in combination with the prediction results, and obtain the optimal control vector; The reactive power regulation instruction acquisition module is used to construct a linear discretization state space model of voltage control, and convert the model into a quadratic programming problem for solution to obtain the optimal reactive power regulation instruction for each adjustable reactive resource of the microgrid; The control and regulation module controls and regulates the microgrid based on the optimal control vector and the optimal reactive power regulation instruction, monitors the state of the microgrid in real time, and dynamically adjusts the weight coefficients of the multi-objective optimization model to optimize the energy efficiency of the microgrid.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

Images