Dynamic aggregation control method and device for micro-grid group participating in primary frequency modulation

Abstract

The invention provides a dynamic aggregation control method and device for a micro-grid group to participate in primary frequency modulation, and relates to the technical field of power grid frequency modulation control. The method comprises the following steps: establishing a frequency control target of a micro-grid group based on a K-medoids aggregation frequency control model, and establishing a minimum aggregation unit model; on the basis of the aggregation condition, carrying out on-line solving on the direct current gain of each device, and obtaining transmission characteristics required by each minimum aggregation unit according to a frequency control target; and constructing a controller model corresponding to each device based on the transmission characteristics required by each minimum aggregation unit, and solving control parameters by using each controller model so as to realize dynamic aggregation control of the micro-grid group participating in primary frequency modulation. According to the dynamic aggregation control method and device for the micro-grid group to participate in the primary frequency modulation, the dynamic response capability of the primary frequency modulation of the micro-grid group can be improved.

Description

Technical Field

[0001] This invention relates to the field of power grid frequency regulation control technology, specifically to a dynamic aggregation control method and device for microgrid groups participating in primary frequency regulation. Background Technology

[0002] With the increasing integration of distributed energy sources, new energy storage technologies, and controllable loads into microgrids, the flexibility and adjustability of microgrid operations have been enhanced, providing new possibilities for rapid frequency support. However, due to the dispersed access, varying output characteristics, and relatively small capacity of the adjustable resources within a microgrid, joint participation in distribution network secondary frequency regulation is challenging. Based on the traditional microgrid architecture, various types of resources within a microgrid can be aggregated to form a microgrid cluster and uniformly regulated. This cluster exhibits predetermined aggregation characteristics externally and achieves rapid power allocation and absorption internally, constructing a novel multi-microgrid joint system with multiple functions such as distributed resource management and frequency regulation.

[0003] Traditional distribution network frequency regulation schemes focus on the optimal scheduling and benefit distribution of multiple stakeholders. However, microgrid clusters encompass a larger area of ​​multiple microgrids working together. In the design of control strategies, it is necessary to consider not only the ancillary services that conventional microgrid clusters can provide, but also factors such as internal topology. The focus is on the dynamic coordinated regulation of multiple types of distributed resources to improve the dynamic response capability of multiple microgrids in ancillary service scenarios such as frequency regulation and voltage regulation. It has the characteristics of being fast, flexible, and economical. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a dynamic aggregation control method and apparatus for microgrid groups participating in primary frequency regulation, which can at least partially solve the problems existing in the prior art.

[0005] On the one hand, this invention proposes a dynamic aggregation control method for microgrid groups participating in primary frequency regulation, comprising: The frequency control target of the microgrid group is established based on the K-medoids aggregation frequency control model, and the minimum aggregation unit model is established. Based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required for each minimum aggregation unit are obtained according to the frequency control target. Based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0006] The step of establishing the frequency control objective of the microgrid group based on the K-medoids aggregation frequency control model and establishing the minimum aggregation unit model includes: The minimum aggregation unit model is determined based on the local closed-loop transfer function corresponding to each device and the aggregation point frequency deviation of all devices.

[0007] The step of online solving for the DC gain of each device based on the aggregation conditions, and obtaining the transmission characteristics required for each minimum aggregation unit according to the frequency control target, includes: Based on the preset transfer function and device type, the local closed-loop transfer function corresponding to each device is decomposed to obtain the online adaptive dynamic factor corresponding to each device. The online adaptive dynamic factor for low-pass filters is determined to be the online adjustable DC gain. An objective function is constructed with the goal of minimizing the total weighted DC gain. The objective function is solved based on the constraints determined by the online adjustable DC gain and the online adaptive dynamic factor to obtain the optimal solution for the DC gain of each device. The optimal solution for the DC gain of each device and the time-varying power capacity limit that varies with external factors are updated in a distributed consensus manner to obtain the preset value of the DC gain at the next moment, so as to reflect the transmission characteristics required by each minimum aggregation unit.

[0008] The construction of the objective function with the goal of minimizing the total weighted DC gain includes: The objective function is constructed based on the online adjustable DC gain, the equipment adjustment cost of each device, and the time-varying active power capacity limit of each device.

[0009] The optimal solution for the DC gain of each device and the time-varying power capacity limit that varies with external factors are updated in a distributed consensus manner, including: If it is determined that the consistency index of all devices converges to the same value, then the optimal solution of the DC gain of each device is updated according to the time constant, the weight between the DC gains of adjacent devices, and the consistency index.

[0010] The construction of a controller model corresponding to each device based on the transmission characteristics required by each minimum aggregation unit includes: The constructed controller model consists of three parts: a prediction model, rolling optimization, and feedback correction.

[0011] On the one hand, the present invention proposes a dynamic aggregation control device for microgrid groups participating in primary frequency regulation, comprising: Establish a unit to establish the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model, and establish the minimum aggregation unit model; The acquisition unit is used to solve the DC gain of each device online based on the aggregation conditions, and to obtain the transmission characteristics required by each minimum aggregation unit according to the frequency control target. The control unit is used to construct a controller model corresponding to each device based on the transmission characteristics required by each smallest aggregation unit, and to solve the control parameters using each controller model in order to realize the dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0012] In another aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the following method: The frequency control target of the microgrid group is established based on the K-medoids aggregation frequency control model, and the minimum aggregation unit model is established. Based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required for each minimum aggregation unit are obtained according to the frequency control target. Based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0013] This invention provides a computer-readable storage medium, comprising: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method: The frequency control target of the microgrid group is established based on the K-medoids aggregation frequency control model, and the minimum aggregation unit model is established. Based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required for each minimum aggregation unit are obtained according to the frequency control target. Based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0014] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the following method: The frequency control target of the microgrid group is established based on the K-medoids aggregation frequency control model, and the minimum aggregation unit model is established. Based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required for each minimum aggregation unit are obtained according to the frequency control target. Based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0015] The present invention provides a dynamic aggregation control method and apparatus for microgrid groups participating in primary frequency regulation. Based on a K-medoids aggregation frequency control model, a frequency control target for the microgrid group is established, and a minimum aggregation unit model is created. Based on aggregation conditions, the DC gain of each device is solved online, and the required transmission characteristics of each minimum aggregation unit are obtained according to the frequency control target. Based on the required transmission characteristics of each minimum aggregation unit, a controller model corresponding to each device is constructed, and control parameters are solved using each controller model to achieve dynamic aggregation control of the microgrid group participating in primary frequency regulation, thereby improving the dynamic response capability of the microgrid group in primary frequency regulation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart illustrating a dynamic aggregation control method for microgrid groups participating in primary frequency regulation, provided in an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the local dynamic feedback control architecture provided in an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of the structure of a dynamic aggregation control device for microgrid groups participating in primary frequency regulation, provided in an embodiment of the present invention.

[0019] Figure 4 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0021] Figure 1 This is a flowchart illustrating a dynamic aggregation control method for microgrid groups participating in primary frequency regulation according to an embodiment of the present invention, as shown below. Figure 1 As shown in the embodiment of the present invention, the dynamic aggregation control method for microgrid groups participating in primary frequency regulation includes: Step S1: Establish the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model, and establish the minimum aggregation unit model.

[0022] Step S2: Based on the aggregation conditions, solve the DC gain of each device online, and obtain the transmission characteristics required for each minimum aggregation unit according to the frequency control target.

[0023] Step S3: Based on the transmission characteristics required by each minimum aggregation unit, construct a controller model corresponding to each device, and use each controller model to solve the control parameters to realize the dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0024] In step S1 above, the device establishes the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model and establishes the minimum aggregation unit model. The device can be a computer device that executes this method. The acquisition, storage, use, and processing of data in the technical solution of this application all comply with relevant regulations.

[0025] The frequency aggregation control of this invention is designed for primary frequency regulation scenarios in power grids. It employs the K-medoids clustering algorithm instead of the traditional K-means clustering algorithm, reducing outlier sensitivity by selecting central samples as cluster centers. It assumes that all distributed resources (devices) are connected to the same bus in the transmission network, and these devices receive frequency deviations from aggregation points (POCs). f POC This serves as the input signal. The active power deviation output of each device i is... p i (Deviating from the specified power reference point for each device), the total active power deviation output of all devices aggregated into the smallest aggregation unit is... p k The relevant formulas are as follows: (1) (2) The local closed-loop transfer function Ti(s) captures all the dynamic characteristics of the decoupled active power loop from active power measurement to applied frequency in the system, namely the dynamics of the power converter, filter, grid-side converter control loop, DC-side, and power supply characteristics (such as wind turbines or photovoltaic systems), and is reflected in the local controller model so that the converter control model can track its reference characteristics. It includes all controllable distributed power sources participating in the aggregation at the smallest aggregation unit.

[0026] To obtain a simplified converter model that can be used for subsequent control strategy design, the inverter model needs to be linearized and its order appropriately reduced to design the feedback gain and optimize the solution of the subsequent control loop. Considering the dynamic characteristics of the AC side, including filter dynamics, current loop, phase-locked loop (PLL), and power calculation, the formulas are as follows: (3) (4) (5) (6) in, k p,i and k i,i These represent the proportional and integral coefficients in a proportional-integral (PI) controller, respectively; ωb is the reference frequency; Zk is the equivalent impedance; and Lk is the filter inductance. v ctrl,dq and i dq Let be the voltage and current in direct / quadrature axis (dq) form of the control loop output, respectively, based on the assumption that: vd ≈v The fact that vq≈0 allows for the decoupling of the active and reactive power expressions. Furthermore, it is assumed that the system approximately maintains its rated operating condition. v ctrl,dq ≈ v ctrl,dq By linearizing the system near its rated operating point and focusing on the relationship between id and active power, the deviations of each variable from its respective equilibrium point can be obtained, such as: i dq =i dq –i dq , 0, p=pp 0, x i,d =x i,d -x i,d,0 . X i,dq It is the integral state variable of the current loop. v dq This is the actual voltage across the dq axis. x id Let be the rate of change of the integral state variable of the current loop.

[0027] In step S2 above, the device performs online calculation of the DC gain of each device based on the aggregation conditions, and obtains the transmission characteristics required for each minimum aggregation unit according to the frequency control objective. Based on the dynamic frequency aggregation model of the microgrid group, the required transmission characteristics can be decomposed to each device by adding local matching conditions, as shown in the following equation: (7) in, n i ( s ) is an online adaptive dynamic factor, that is, an adaptive dynamic participation characteristic that includes fp relationship.

[0028] Combining the aggregation conditions, we can obtain: (8) Considering the solvability of local matching conditions, the characteristics of each reference model must be carefully selected to ensure dynamic matching by associated devices (equipment) under normal operating conditions. On one hand, this requires pre-setting the transfer function. On the one hand, it is pre-set reasonably; on the other hand, it also needs to be selected based on the dynamic characteristics and limitations of individual devices. n i ( s Meanwhile, the dynamics of local distributed power sources, various time scales, and steady-state power capacity limitations under normal operating conditions are also taken into account.

[0029] Therefore, OADF (Optical Auto Detection and Feedback) is determined by multiple parameters, including the time constant αi, which takes into account the time scale of different distributed power dynamics; and the DC gain βi, which takes into account the maximum power capacity limit and regulation cost available to the device in steady state, similar to the traditional droop control gain or static allocation coefficient. K SOC,i A coefficient is used to account for the state of charge (SOC) of energy storage. Based on this, it can be... n i ( s The forms are divided into the following three categories: For devices that provide steady-state gain adjustment over longer timescales (such as most distributed resources like photovoltaics and wind turbines, and flexible loads represented by electric vehicle charging stations), there exists a dynamic factor similar to a low-pass filter (LPF), namely: (9) For devices capable of providing regulation on rapid timescales (such as power storage devices that rapidly release and store energy, like supercapacitors), there exists a dynamic factor similar to a high-pass filter (HPF) (for non-low-pass devices, the regulation cost is approximately the same due to the very short output time), namely: (10) in, To account for the coefficient of energy storage state of charge, when λ SOC,i ∈[0, λ SOC,min ], K SOC,i =0; when λ SOC,i ∈[ λ SOC,min , λ SOC,max ], ; λ SOC,i ∈[ λ SOC,max 1], K SOC,i = 1. Wherein, λ SOC,i For equipment i The energy storage state of charge; λ SOC,min and λ SOC,max This indicates the minimum and maximum values ​​for the energy storage state of charge setting.

[0030] For residual devices with dynamic characteristics falling between the two categories mentioned above (such as power storage devices like batteries), there exists a dynamic factor similar to a band-pass filter (BPF), namely: (11) in, α i and α j They are the first i , j The response rate coefficient of each inertial element. It is important to note that OADFs with BPF or HPF characteristics always have a constant zero DC gain under the defined condition, i.e. n i (0) = 0. Conversely, for all OADF devices with similar LPF characteristics, the following equation must be satisfied: (12) Based on this, the DC gain of LPF-type devices is defined as follows: β i This allows for online adjustment, proportional to the time-varying power capacity limit of the equipment, and considers the regulation cost of each power supply. Employing the optimal Quadratic Assignment Problem (QAP), the optimal DC gain value for each device can be quickly and easily obtained, while minimizing the total weighted DC gain as much as possible. (13) Among them, the equality constraint guarantees that the expression holds. Represents a set of uncontrollable devices In the relationship between active power and frequency T i ( s The sum of DC gains, which is constant and can be determined by factors such as the droop characteristics of a synchronous generator. Adjusting equipment costs Indicates equipment i The time-varying active power capacity limit is located at the minimum limit of the equipment at that time. and maximum limit The time-varying value of the DC gain is represented by the optimal solution in QAP form, as follows: (14) Among them, the DC gain of LPF type equipment n i ( t ) in the interval [ n i_low ( t ), n i_max ( t The upper bound is defined as follows: n i_max ( t and lower bound n i_low ( t It depends on the upper and lower bounds of the power capacity.

[0031] thus, n i ( t The power capacity limits are proportional to the power capacity limits of each controllable device. It is worth noting that these limits are likely time-varying due to complex factors such as climate. During power system operation, DC gain and active power capacity limits are updated using a distributed consensus mechanism. Taking DC gain as an example, the formula is as follows: (15) in,β i ( t+ 1) Represents the preset value of DC gain at the next moment; λ i,j This indicates the weighting between the DC gains of adjacent devices; τ j ( t () is a consistency indicator; μ It is a time constant. When all devices τ When the convergence reaches a common value, it is considered that a consensus has been reached and the value has been determined. β i ( t+ The value of 1) is used as the next time step. β i ( t ).

[0032] In step S3 above, the device constructs a controller model corresponding to each device based on the transmission characteristics required by each minimum aggregation unit, and uses each controller model to solve the control parameters in order to realize the dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0033] The goal of a local dynamic feedback controller (controller model) is to design a matched controller for each controllable device (device) so that its characteristics match the desired local performance. Its function is to ensure that, in the presence of DC gain variations, uncertainties in the steady-state model of the actual device, and nonlinear factors, the controller can optimally match the locally desired characteristics obtained by each device while satisfying device-level current constraints.

[0034] This invention uses model predictive control to solve these local matching control design problems. It is relatively simple to process computationally and provides a suboptimal guarantee for solving the DC gain, thereby limiting the weakening of the control performance and the exceeding of transient indicators under various operating conditions.

[0035] Model predictive control (MMCC) consists of three parts: a predictive model, rolling optimization, and feedback correction. In this algorithm, the predictive model predicts the system's output for future stages based on current and past input / output variables and changes in the control variable. The rolling optimization model constructs an objective function and related constraints based on the deviation between the expected and predicted output values, optimizing the optimal control sequence for the controlled object within a finite time domain. In rolling optimization, only the optimization result of the first time period in the optimal control sequence is executed, thereby improving the real-time performance and accuracy of the system control. The feedback correction model collects the real-time values ​​of the system output through secondary equipment at each sampling time and feeds these real-time values ​​back to the predictive model, improving the prediction accuracy of the predictive model.

[0036] A predictive model uses historical information and future inputs of the controlled object to predict the future output of the system. Model predictive control does not impose strict restrictions on the form of the predictive model; therefore, any model that can predict the future state of the system can be used as a predictive model. This invention establishes a predictive model for line loss, voltage, and real-time operating costs based on a multiple sensitivity coefficient matrix, as shown in the following equation: (16) (17) (18) in, i To predict the number of steps, i= 1,2,3, …,N p -1 ,N p ; V ( k+i | k )for k Predicting the future k+i Voltage amplitude at any given moment; P lossij ( k+i | k )for k Predicting the future k+i Line loss at any given moment; C re ( k+i | k )for k Predicting the future k +i The value of the operating cost function at time Δ P re Δ Q re for k+i The adjustable equipment at time -1 needs to optimize the control of changes in active power and reactive power.

[0037] Δ P re = [Δ P PV,w,re Δ P EV , r,re ], Δ Q re = [Δ Q PV , w,re Δ QSVC , n,re That is, the active power control variable of the regulating equipment includes the photovoltaic active power reduction Δ. P PV , w,re Δ active power regulation of electric vehicles P EV , r,re The reactive power control variables of the regulating equipment include the photovoltaic reactive power regulation amount Δ. Q PV , w,re SVC reactive power regulation Δ Q SVC , n,re ; S Plossij-P This is the sensitivity matrix of network loss to active power. S Plossij-Q This is the sensitivity matrix of network loss to reactive power. S U-P This is the voltage sensitivity matrix to active power. S U-Q This is the voltage sensitivity matrix to reactive power. S c-P Let $\mathbf{ ... S c-PR The sensitivity matrix of cost to reactive power at rc nodes. S c-QR This is the sensitivity matrix of cost to reactive power at the rc node. Δ P rc Δ Q rc This is the corresponding real-time power correction amount for the adjustable device, and its value is obtained through... k+i The adjustable equipment power at any given time is obtained by subtracting the power for that time period from the daily scheduling plan, as shown in the following formula: (19) (20) in, P ( k+i | k ), Q ( k+i | k )for k Predicting the future k+i Adjustable equipment output at specific times; P ( k+i ), Q ( k+i(This refers to the intraday scheduling plan) k+i Adjustable equipment output at any time.

[0038] In the rolling optimization process, this chapter establishes a real-time optimization model with the objectives of minimizing line losses of measurable lines and minimizing real-time operating costs of adjustable equipment. Using a 3-minute time scale, the model continuously optimizes the sequence of control variables for future finite time periods, helping to smooth out real-time fluctuations in distributed resources and further ensuring the safe and economical operation of the microgrid group, as shown in the following equation: (twenty one) in, P lossij It can be obtained from calculation; C re It can be calculated to obtain; o For the first o A line that can be measured in real time. O A collection of lines that can be measured in real time; tou This refers to the real-time electricity price.

[0039] The effectiveness of voltage optimization is measured by voltage deviation, as shown in the following formula: (twenty two) in, V bais This is the voltage deviation value; V i , V i,ref Here, we have the node voltage and its reference value. The rolling optimization model must satisfy operational constraints, and the nodes that can be measured in real time must also satisfy voltage safety constraints. To further reduce the impact of randomness and volatility on scheduling in real-time optimization, the real-time measurement data of the new round of system is used as the initial state for the new round of rolling optimization, thus forming a closed-loop feedback, as shown in the following equation: (twenty three) in, X 0( k )for k The initial state value during the rolling optimization of the time system; X real ( k- 1) For kAt time -1, the system can measure the actual voltage of nodes and the actual line loss of lines in real time. Simultaneously, to reduce the adverse impact of sensitivity errors caused by changes in the system's operating point on dispatch reliability, a sensitivity error correction feedback is added, forming a closed-loop feedback mechanism together with the aforementioned closed loop. This new closed-loop feedback mechanism receives the actual measured values ​​of the system's real-time measurable nodes in real time and outputs the corrected adjustable device node voltage / line loss sensitivity. Given that the power supply range of the microgrid group is relatively small, and that most of them belong to the same management and operation agency within this specific power supply range, they all adopt a unified real-time electricity price to update the real-time adjustment cost sensitivity portion of the cost sensitivity, thereby enhancing the economy and reliability of the system's real-time dispatch.

[0040] like Figure 2 The local dynamic feedback control architecture is described below: L f , R f For the values ​​of the filter inductor and resistor, L line , R line For the value of line inductance and resistance, i abc The three-phase current at the grid connection point, v abc The three-phase voltage at the grid connection point, i dq The three-phase current is obtained by changing the park. d shaft and q Axial components, v dq The d-axis and q-axis components are obtained by transforming the three-phase voltage using Park. PLL is a phase-locked loop, and PI is a proportional-integral control. w PLL The phase-locked loop outputs angular velocity. x idq Let be the integral state variable of the current loop. i dq After being controlled d shaft and q Axis current component output θ PLL This is the output angle of the phase-locked loop.

[0041] The dynamic aggregation control method for microgrid groups participating in primary frequency regulation provided in this invention utilizes microgrid groups to participate in ancillary services, making full use of the distributed resources in the large number of existing microgrids. Unlike frequency regulation of a single power station, it expands the controllable resources of the power system and increases its participation in system regulation. At the methodological strategy level, existing literature does not clearly define the aggregation control design of each distributed energy source, thus failing to utilize the heterogeneity of distributed resources to achieve the aggregation behavior required for dynamic ancillary services. This results in poor performance and reliability during the provision of ancillary services. Microgrid groups contain multiple types of distributed resources with power electronic interfaces, possessing second-level flexible adjustment capabilities and optimized frequency characteristics. Based on this, a dynamic frequency K-medoids aggregation control strategy is proposed. This method considers the aggregation of different types of distributed energy sources, models them at the device level, and utilizes their complementary characteristics in terms of adjustment amplitude, response time, and duration for primary frequency regulation. Furthermore, based on the adaptive dynamic participation factor design approach, the limitations of time variables (such as capacity, response time, weather factors, cost, etc.) are considered, as well as spatial topology. Model predictive control methods are used to reduce control errors, increase disturbance rejection capabilities, and accurately match the desired frequency regulation characteristics, providing a new approach for the joint participation of multiple microgrids in distribution network frequency regulation.

[0042] The dynamic aggregation control method for microgrid groups participating in primary frequency regulation provided in this invention has the following advantages: The adaptive dynamic frequency K-medoids aggregation control strategy proposed in this invention, based on factors such as capacity, response time, weather, and cost, uses online adaptive dynamic factor decomposition to determine the desired characteristics and employs model predictive control methods to design local feedback controllers for each device, thereby optimally matching the frequency response characteristics expected by the microgrid group participating in dynamic auxiliary services.

[0043] The present invention enables multiple types of distributed resources with different characteristics to complement each other in order to improve performance.

[0044] The dynamic aggregation control method for microgrid groups participating in primary frequency regulation provided in this invention has the following beneficial technical effects: Flexible and robust K-medoids aggregation control strategies are fundamental to realizing dynamic frequency regulation ancillary services. This invention proposes a control method for multiple microgrids jointly participating in distribution network frequency regulation. Considering factors such as capacity, response time, weather, and cost, it utilizes online adaptive dynamic factor decomposition of desired characteristics and employs model predictive control to design local feedback controllers for each device. This optimally matches the frequency response characteristics expected by the microgrid group participating in dynamic ancillary services. Furthermore, various types of distributed resources with different characteristics can complement each other to improve performance. Final simulation results show that, under load disturbances and sudden changes in renewable energy output limits, the proposed control strategy improves the minimum frequency value (>0.02Hz) compared to traditional methods, enhances the primary frequency regulation characteristics of the power grid, and, considering actual equipment constraints and spatial distribution, ensures that the active power output curve accurately meets the desired aggregation characteristic curve with good dynamic performance. This provides an effective solution for microgrids participating in dynamic frequency regulation ancillary services.

[0045] The dynamic aggregation control method for microgrid groups participating in primary frequency regulation provided in this invention establishes the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model and establishes a minimum aggregation unit model; based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required by each minimum aggregation unit are obtained according to the frequency control target; based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize the dynamic aggregation control of the microgrid group participating in primary frequency regulation, which can improve the dynamic response capability of the microgrid group in primary frequency regulation.

[0046] In the above optional embodiments, the step of establishing the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model and establishing the minimum aggregation unit model includes: The minimum aggregation unit model is determined based on the local closed-loop transfer function corresponding to each device and the aggregation point frequency deviation of all devices. This can be referred to the above embodiments for explanation, and will not be repeated here.

[0047] In the above optional embodiments, the step of solving the DC gain of each device online based on the aggregation conditions, and obtaining the transmission characteristics required for each minimum aggregation unit according to the frequency control target, includes: Based on the preset transfer function and device type, the local closed-loop transfer function corresponding to each device is decomposed to obtain the online adaptive dynamic factor corresponding to each device; the above embodiments can be referred to for explanation, and will not be repeated here.

[0048] The online adaptive dynamic factor for the device type, which is a low-pass filter, is determined to be an online adjustable DC gain. An objective function is constructed with the goal of minimizing the total weighted DC gain. The objective function is solved based on the constraints determined by the online adjustable DC gain and the online adaptive dynamic factor to obtain the optimal solution for the DC gain of each device. This can be referred to the above embodiment for explanation, and will not be repeated here.

[0049] The optimal solution for the DC gain of each device and the time-varying power capacity limit that varies with external factors are updated using a distributed consensus mechanism to obtain the preset value of the DC gain for the next moment, reflecting the transmission characteristics required by each minimum aggregation unit. This can be referred to the above embodiments for further explanation and will not be repeated here.

[0050] In the above optional embodiments, constructing the objective function with the goal of minimizing the total weighted DC gain includes: The objective function is constructed based on the online adjustable DC gain, the equipment regulation cost of each device, and the time-varying active power capacity limit of each device. This can be referred to the above embodiments for explanation, and will not be repeated here.

[0051] In the above optional embodiments, the optimal solution for the DC gain of each device and the time-varying power capacity limit that varies with external factors are updated in a distributed consensus manner, including: If it is determined that the consistency index of all devices converges to the same value, then the optimal solution for the DC gain of each device is updated based on the time constant, the weight between the DC gains of adjacent devices, and the consistency index. This can be referred to the above embodiment for explanation, and will not be repeated here.

[0052] In the above optional embodiments, the step of constructing a controller model corresponding to each device based on the transmission characteristics required by each minimum aggregation unit includes: The constructed controller model consists of three parts: a prediction model, rolling optimization, and feedback correction. These can be referred to the above embodiments for explanation, and will not be repeated here.

[0053] Figure 3 This is a schematic diagram of the structure of a dynamic aggregation control device for microgrid groups participating in primary frequency regulation according to an embodiment of the present invention, as shown below. Figure 3 As shown, the dynamic aggregation control device for microgrid groups participating in primary frequency regulation provided in this embodiment of the invention includes an establishment unit 301, an acquisition unit 302, and a control unit 303, wherein: The establishment unit 301 is used to establish the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model, and to establish the minimum aggregation unit model; the acquisition unit 302 is used to solve the DC gain of each device online based on the aggregation conditions, and to obtain the transmission characteristics required by each minimum aggregation unit according to the frequency control target; the control unit 303 is used to construct the controller model corresponding to each device based on the transmission characteristics required by each minimum aggregation unit, and to solve the control parameters using each controller model, so as to realize the dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0054] Specifically, the establishment unit 301 in the device is used to establish the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model, and to establish the minimum aggregation unit model; the acquisition unit 302 is used to solve the DC gain of each device online based on the aggregation conditions, and to obtain the transmission characteristics required by each minimum aggregation unit according to the frequency control target; the control unit 303 is used to construct the controller model corresponding to each device based on the transmission characteristics required by each minimum aggregation unit, and to solve the control parameters using each controller model, so as to realize the dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0055] The dynamic aggregation control device for microgrid groups participating in primary frequency regulation provided in this invention establishes the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model and establishes the minimum aggregation unit model; based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required by each minimum aggregation unit are obtained according to the frequency control target; based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize the dynamic aggregation control of the microgrid group participating in primary frequency regulation, which can improve the dynamic response capability of the microgrid group in primary frequency regulation.

[0056] The embodiments of the present invention provide a dynamic aggregation control device for microgrid groups participating in primary frequency regulation, which can be used to execute the processing flow of the above-described method embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above-described method embodiments.

[0057] Figure 4 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention, such as... Figure 4 As shown, the computer device includes: a memory 401, a processor 402, and a computer program stored in the memory 401 and executable on the processor 402. When the processor 402 executes the computer program, it implements the following method: The frequency control target of the microgrid group is established based on the K-medoids aggregation frequency control model, and the minimum aggregation unit model is established. Based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required for each minimum aggregation unit are obtained according to the frequency control target. Based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0058] This embodiment discloses a computer program product, which includes a computer program that, when executed by a processor, implements the following method: The frequency control target of the microgrid group is established based on the K-medoids aggregation frequency control model, and the minimum aggregation unit model is established. Based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required for each minimum aggregation unit are obtained according to the frequency control target. Based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0059] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the following method: The frequency control target of the microgrid group is established based on the K-medoids aggregation frequency control model, and the minimum aggregation unit model is established. Based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required for each minimum aggregation unit are obtained according to the frequency control target. Based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize dynamic aggregation control of the microgrid group participating in primary frequency regulation.

[0060] Compared with existing technologies, the dynamic aggregation control method for microgrid groups participating in primary frequency regulation provided in this invention establishes the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model and establishes a minimum aggregation unit model. Based on the aggregation conditions, the DC gain of each device is solved online, and the required transmission characteristics of each minimum aggregation unit are obtained according to the frequency control target. Based on the required transmission characteristics of each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to achieve dynamic aggregation control of the microgrid group participating in primary frequency regulation, thereby improving the dynamic response capability of the microgrid group in primary frequency regulation.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0066] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic aggregation control method for microgrid groups participating in primary frequency regulation, characterized in that, include: The frequency control target of the microgrid group is established based on the K-medoids aggregation frequency control model, and the minimum aggregation unit model is established. Based on the aggregation conditions, the DC gain of each device is solved online, and the transmission characteristics required for each minimum aggregation unit are obtained according to the frequency control target. Based on the transmission characteristics required by each minimum aggregation unit, a controller model corresponding to each device is constructed, and the control parameters are solved using each controller model to realize dynamic aggregation control of the microgrid group participating in primary frequency regulation.

2. The dynamic aggregation control method for microgrid groups participating in primary frequency regulation according to claim 1, characterized in that, The frequency control objective of the microgrid group is established based on the K-medoids aggregation frequency control model, and a minimum aggregation unit model is established, including: The minimum aggregation unit model is determined based on the local closed-loop transfer function corresponding to each device and the aggregation point frequency deviation of all devices.

3. The dynamic aggregation control method for microgrid groups participating in primary frequency regulation according to claim 2, characterized in that, The online solution for the DC gain of each device based on the aggregation conditions, and the determination of the transmission characteristics required for each minimum aggregation unit according to the frequency control target, include: Based on the preset transfer function and device type, the local closed-loop transfer function corresponding to each device is decomposed to obtain the online adaptive dynamic factor corresponding to each device. The online adaptive dynamic factor for low-pass filters is determined to be the online adjustable DC gain. An objective function is constructed with the goal of minimizing the total weighted DC gain. The objective function is solved based on the constraints determined by the online adjustable DC gain and the online adaptive dynamic factor to obtain the optimal solution for the DC gain of each device. The optimal solution for the DC gain of each device and the time-varying power capacity limit that varies with external factors are updated in a distributed consensus manner to obtain the preset value of the DC gain at the next moment, so as to reflect the transmission characteristics required by each minimum aggregation unit.

4. The dynamic aggregation control method for microgrid groups participating in primary frequency regulation according to claim 3, characterized in that, The objective function constructed with the goal of minimizing the total weighted DC gain includes: The objective function is constructed based on the online adjustable DC gain, the equipment adjustment cost of each device, and the time-varying active power capacity limit of each device.

5. The dynamic aggregation control method for microgrid groups participating in primary frequency regulation according to claim 3, characterized in that, The optimal solution for the DC gain of each device and the time-varying power capacity limit that varies with external factors are updated in a distributed consensus manner, including: If it is determined that the consistency index of all devices converges to the same value, then the optimal solution of the DC gain of each device is updated according to the time constant, the weight between the DC gains of adjacent devices, and the consistency index.

6. The dynamic aggregation control method for microgrid groups participating in primary frequency regulation according to any one of claims 1 to 5, characterized in that, The construction of a controller model corresponding to each device based on the transmission characteristics required by each minimum aggregation unit includes: The constructed controller model consists of three parts: a prediction model, rolling optimization, and feedback correction.

7. A dynamic aggregation control device for microgrid groups participating in primary frequency regulation, characterized in that, include: Establish a unit to establish the frequency control target of the microgrid group based on the K-medoids aggregation frequency control model, and establish the minimum aggregation unit model; The acquisition unit is used to solve the DC gain of each device online based on the aggregation conditions, and to obtain the transmission characteristics required by each minimum aggregation unit according to the frequency control target. The control unit is used to construct a controller model corresponding to each device based on the transmission characteristics required by each smallest aggregation unit, and to solve the control parameters using each controller model in order to realize the dynamic aggregation control of the microgrid group participating in primary frequency regulation.

8. 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, it implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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