Distributed frequency modulation method and related device

Abstract

The method comprises the following steps: constructing a node frequency response model of a micro-grid based on a frequency fluctuation space distribution characteristic of the micro-grid; constructing a first optimization model with a minimum operation cost of the micro-grid as an optimization objective and with inertia stability of the micro-grid as a constraint condition; constructing a plurality of second optimization models with a maximum energy yield of a distributed energy source as an optimization objective and with a reserved energy constraint of the distributed energy source based on the inertia stability of the micro-grid; screening a target fault scenario of each node; performing nested optimization based on the target fault scenario of each node, combining the first optimization model and the plurality of second optimization models, and obtaining a micro-grid frequency modulation optimization model to perform frequency modulation control on the micro-grid and determine inertia provided by each node. The present disclosure can provide frequency regulation support when the power grid frequency fluctuates, and improve the operation stability of the power system.

Description

Technical Field

[0001] This disclosure relates to the field of power system frequency security, and more specifically, to a distributed frequency regulation method and related equipment. Background Technology

[0002] With the global energy structure transformation and the rapid development of renewable energy, traditional power systems face the severe challenge of insufficient inertia support. Inertia is an important factor in system frequency stability and is mainly provided by traditional generator units, especially thermal and hydropower units, which can use the inertia of rotating machinery to resist frequency changes.

[0003] However, as the proportion of renewable energy sources such as wind and solar power increases, traditional generator sets are gradually being phased out of the power grid, leading to a decrease in the inertia level of the power system and insufficient frequency regulation capability. This may cause grid instability or even collapse, especially during sudden load changes or fluctuations in renewable energy. Summary of the Invention

[0004] In this context, embodiments of the present disclosure aim to provide a distributed frequency regulation method and related equipment to provide frequency regulation support when the grid frequency fluctuates, mitigate the negative impact of inertia reduction, and improve the safety and stability of power system operation.

[0005] In a first aspect of this disclosure, a distributed frequency modulation method is provided, comprising: Based on the spatial distribution characteristics of frequency fluctuations in a microgrid, a node frequency response model for the microgrid is constructed; the microgrid includes multiple nodes. Based on the node frequency response model, with the minimum operating cost of the microgrid as the optimization objective and the inertial stability of the microgrid as the constraint, a first optimization model is constructed. Based on the node frequency response model, with the optimization objective of maximizing the energy revenue of distributed energy sources, and based on the inertia stability of the microgrid, the reserved energy constraints of the distributed energy sources are constructed, and multiple second optimization models are built; wherein, each node corresponds to one distributed energy source. Based on the historical operation data of the nodes, the target failure scenarios for each node are selected. Based on the target fault scenario of each node, nested optimization is performed by combining the first optimization model and the multiple second optimization models to obtain the microgrid frequency regulation optimization model; Based on the power grid frequency regulation optimization model, frequency regulation control is performed on the microgrid to determine the inertia provided by each node.

[0006] In some possible embodiments of this disclosure, the plurality of nodes includes at least one load node and at least one power node; Based on the aforementioned power grid frequency regulation optimization model, frequency regulation control is performed on the microgrid, including: Based on the derivation of the mapping relationship between the frequency indices of the load nodes and the power supply nodes, the nonlinear constraints of the power grid frequency regulation optimization model are relaxed and linearized to obtain a simplified power grid frequency regulation optimization model; wherein, the simplified power grid frequency regulation optimization model is a single-layer mixed integer quadratic programming model. The simplified power grid frequency regulation optimization model is solved to obtain the frequency regulation control strategy of the microgrid.

[0007] In some possible embodiments of this disclosure, the power grid frequency regulation optimization model is an optimization model of a two-level problem, which includes an upper-level problem and a lower-level problem; Specifically, based on the mapping relationship between the frequency indices of the load nodes and the power supply nodes, the nonlinear constraints of the power grid frequency regulation optimization model are relaxed and linearized to obtain a simplified power grid frequency regulation optimization model, including: Based on the mapping relationship, the nonlinear constraints of the frequency index of the load node are relaxed, and the upper-level problem is linearized. By adding the KKT conditions of the lower-level problem to the constraints of the upper-level problem after linearization, a simplified power grid frequency regulation optimization model is obtained.

[0008] In some possible embodiments of this disclosure, the frequency fluctuation spatial distribution characteristics include: the frequency response of the microgrid is achieved by the power nodes through adjusting inertia, and the load nodes are passively affected by the adjustment of the power nodes; Based on the spatial distribution characteristics of frequency fluctuations in a microgrid, a node frequency response model for the microgrid is constructed, including: Based on the frequency responses of the load node and the power node, a node frequency response equation is constructed. With the goal of limiting the impact of the microgrid's inertia configuration on the microgrid's frequency security to a safe range, the limiting conditions for the security performance indicators are determined; By combining the node frequency response equation and the constraints of the safety performance indicators, the node frequency response model of the microgrid is obtained.

[0009] In some possible embodiments of this disclosure, the security performance indicators include at least: Load node frequency change rate, load node frequency minimum point, power node frequency change rate, power node frequency minimum point.

[0010] In some possible embodiments of this disclosure, a first optimization model is constructed with the inertial stability of the microgrid as a constraint, including: Based on the frequency regulation recovery characteristics of load nodes after power disturbance and the inertia stability requirements of the microgrid, constraints are established for the frequency change rate of the load nodes, the minimum frequency point of the load nodes, the frequency change rate of the power supply nodes, and the minimum frequency point of the power supply nodes. Based on the inertial excitation constraints of each node, establish inertial excitation constraints. Based on the power balance of the microgrid, power constraints are established.

[0011] In some possible embodiments of this disclosure, based on the target fault scenario of each node, nested optimization is performed by combining the first optimization model and the plurality of second optimization models to obtain a microgrid frequency regulation optimization model, including: Based on the target fault scenario for each node, determine the distribution probability of the power disturbance source; Based on the distribution probability of the power disturbance source, the constraints for correcting the load node frequency change rate, the constraint for the load node frequency minimum point, the constraint for the power node frequency change rate, and the constraint for the power node frequency minimum point are determined.

[0012] In some possible embodiments of this disclosure, when constructing the first optimization model, different inertia excitations are given to different nodes in the microgrid to calculate the operating cost of the microgrid.

[0013] In a second aspect of this disclosure, a distributed frequency modulation device is provided, comprising: A node frequency response model construction unit is used to construct a node frequency response model of the microgrid based on the spatial distribution characteristics of frequency fluctuations; the microgrid includes multiple nodes. The first optimization model construction unit is used to construct a first optimization model based on the node frequency response model, with the minimum operating cost of the microgrid as the optimization objective and the inertia stability of the microgrid as the constraint condition. The second optimization model construction unit is used to construct multiple second optimization models based on the node frequency response model, with the optimization objective of maximizing the energy revenue of distributed energy, and based on the inertia stability of the microgrid to construct the reserved energy constraints of the distributed energy; wherein each node corresponds to one distributed energy. The fault scenario filtering unit is used to filter out the target fault scenarios for each node based on the node's historical operating status. The frequency regulation optimization model construction unit is used to perform nested optimization based on the target fault scenario of each node, combining the first optimization model and the multiple second optimization models, to obtain the microgrid frequency regulation optimization model; The frequency control unit is used to perform frequency control on the microgrid based on the power grid frequency regulation optimization model and to determine the inertia provided by each node.

[0014] In a third aspect of this disclosure, a computing device is provided, the computing device comprising: At least one processor, memory, and input / output unit; The memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the distributed frequency modulation method of the first aspect.

[0015] In a fourth aspect of this disclosure, a computer-readable storage medium is provided, which includes instructions that, when executed on a computer, cause the computer to perform the distributed frequency modulation method of the first aspect.

[0016] In a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the distributed frequency modulation method of the first aspect.

[0017] This disclosure provides a distributed frequency regulation method and related equipment. In this disclosure, a node frequency response model of a microgrid, comprising multiple nodes, is constructed based on the spatial distribution characteristics of frequency fluctuations in the microgrid. Based on the node frequency response model, a first optimization model is constructed with the goal of minimizing the operating cost of the microgrid and the constraint of inertia stability. Based on the node frequency response model, multiple second optimization models are constructed with the goal of maximizing the energy revenue of distributed energy sources and the constraint of reserved energy for distributed energy sources based on the inertia stability of the microgrid. Each node corresponds to one distributed energy source. Based on the historical operating conditions of the nodes, target fault scenarios for each node are selected. Based on the target fault scenarios of each node, nested optimization is performed using the first optimization model and multiple second optimization models to obtain a microgrid frequency regulation optimization model. Based on the microgrid frequency regulation optimization model, frequency regulation control is performed on the microgrid to determine the inertia provided by each node. By constructing a microgrid frequency regulation optimization model, we can accurately guide the rational distribution of inertia resources in the power grid, ensuring that the frequency fluctuations of each node in the microgrid are limited to a safe range. This provides frequency regulation support when the power grid frequency fluctuates, mitigates the negative impact of inertia decline, and improves the safety and stability of power system operation. Attached Figure Description

[0018] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which: Figure 1 A schematic flowchart of a distributed frequency modulation method provided in an embodiment of this disclosure; Figure 2 A schematic diagram illustrating the implementation process of step S110 provided in an optional embodiment of this disclosure; Figure 3 A schematic diagram illustrating the implementation process of step S120 provided in an optional embodiment of this disclosure; Figure 4 A schematic diagram illustrating the implementation process of step S150 provided in an optional embodiment of this disclosure; Figure 5 A schematic diagram illustrating the implementation process of step S160 provided in an optional embodiment of this disclosure; Figure 6 This is a schematic diagram of the structure of a distributed frequency modulation device provided in an embodiment of the present disclosure; Figure 7 A schematic diagram of the structure of a medium according to an embodiment of the present disclosure is shown. Figure 8 A schematic diagram of the structure of a computing device according to an embodiment of the present disclosure is shown.

[0019] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0020] The principles and spirit of this disclosure will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are given merely to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0021] Those skilled in the art will recognize that embodiments of this disclosure can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0022] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0023] like Figure 1 The diagram shown is a flowchart illustrating a distributed frequency modulation method according to an exemplary embodiment of the present disclosure, including the following steps.

[0024] In step S110, a node frequency response model of the microgrid is constructed based on the spatial distribution characteristics of frequency fluctuations in the microgrid.

[0025] It should be noted that microgrids consist of distributed power sources, loads, and intelligent dispatch systems. They can integrate various distributed energy resources (DERs) through coordinated control, providing virtual inertia support for the power grid. Frequency regulation control can proactively respond to changes in power system frequency while providing virtual inertia support. Virtual inertia, by simulating the inertial characteristics of traditional generators, can provide frequency regulation for the power grid without relying on physical rotating machinery. Its flexibility and controllability enable virtual inertia to quickly respond to grid frequency fluctuations and dynamically adjust output power, thereby effectively addressing the problem of frequency instability.

[0026] Understandably, the implementation of virtual inertia is not without cost. It requires distributed energy sources to reserve a certain amount of active power reserve capacity, which will encroach on the output space that can be used for power supply, thus creating a contradiction between system frequency security and efficient resource utilization.

[0027] In embodiments of this disclosure, the microgrid includes multiple nodes, and these nodes include at least one load node and at least one power supply node. The devices at the power supply node can autonomously control their own frequency to satisfy the swing equation of a synchronous generator. Based on the swing equation of the synchronous generator, control methods can be used to provide virtual inertia through control simulation of distributed resources that were previously difficult to provide.

[0028] This disclosure, through the establishment of frequency response models for each node, enables in-depth analysis of the propagation mechanism of frequency fluctuations in microgrids, and establishes models that can accurately characterize the frequency response characteristics of each node, providing a foundation for subsequent quantitative analysis of inertia configuration effects and the construction of optimization models.

[0029] In step S120, based on the node frequency response model, with the goal of minimizing the operating cost of the microgrid and the constraint of the inertial stability of the microgrid, a first optimization model is constructed.

[0030] It should be noted that in a microgrid, frequency changes are not instantaneously and uniformly distributed throughout the network. The node frequency response model describes the dynamic process of node frequency changes over time when the system is subjected to disturbances, such as load surges or generator outages. Understandably, to ensure efficient and reliable operation of the microgrid, its operating costs should be kept as low as possible; therefore, minimizing operating costs is the optimization objective. However, considering the system stability of the microgrid, inertia stability is used as a constraint to ensure that the frequency does not exceed limits, and that the system's inertia reserve is sufficiently large to ensure that, under the worst-case disturbances (such as the shutdown of the largest generator unit), the rate of frequency change and steady-state deviation remain within safe limits. This allows for the establishment of an optimization model for the upper-level microgrid.

[0031] In step S130, based on the node frequency response model, with the optimization objective of maximizing the energy revenue of distributed energy, and based on the inertia stability of the microgrid, a reserved energy constraint for distributed energy is constructed, and multiple second optimization models are built.

[0032] It should be noted that each node corresponds to a distributed energy source. A microgrid contains multiple nodes, each corresponding to a distributed energy source. To ensure that distributed energy sources can obtain greater economic benefits during grid-connected operation, actively participate in the microgrid, and achieve their own efficient utilization, the optimization objective is to maximize the energy revenue of distributed energy sources. It is also necessary to ensure the inertia stability of the microgrid. Therefore, based on the requirement of inertia stability, reserved energy constraints for distributed energy sources can be constructed, and then optimization models corresponding to the lower-level distributed energy sources can be established.

[0033] In step S140, the target failure scenarios for each node are selected based on the node's historical operating status.

[0034] It should be noted that after obtaining the first and second optimization models, the failure scenarios of each node need to be considered for optimization calculations. However, each node has numerous failure types, and calculating each failure individually would significantly increase the computational load and reduce optimization efficiency. This embodiment of the present disclosure can selectively filter failure scenarios based on the node's historical operating data, failure records, and failure occurrence probabilities. Specifically, for each node, the failure types that occur frequently in its historical operation or have a significant impact on system stability are analyzed and identified as the target failure scenarios for that node.

[0035] This disclosure, by selecting key and representative fault scenarios, can effectively reduce the computational scale of the optimization model and improve the real-time performance and feasibility of microgrid optimization scheduling while ensuring system safety.

[0036] In step S150, based on the target fault scenario of each node, nested optimization is performed by combining the first optimization model and multiple second optimization models to obtain the microgrid frequency regulation optimization model.

[0037] It is understandable that the first optimization model is the upper-level problem, and the second optimization model is the lower-level problem. They are coupled and constrained, together forming a two-level programming problem. Specifically, the upper-level first optimization model (minimizing the overall cost of the microgrid) is responsible for formulating the global scheduling strategy. Its decision variables, such as node power allocation and reserve capacity, serve as boundary conditions or constraint parameters for the lower-level problem. Meanwhile, the multiple lower-level second optimization models (maximizing the energy revenue of each node) seek their respective optimal responses within the policy range given by the upper level. This constraint relationship is reflected in the fact that the optimization results of the upper level limit the feasible domain of the lower-level nodes, while the feedback behavior of the lower-level nodes based on maximizing their own interests, in turn, affects the overall actual operating cost and frequency response characteristics of the microgrid. By introducing the KKT (Karush-Kuhn-Tucker) condition to transform the lower-level optimization problem into upper-level constraints, this complex game relationship can be transformed into a single-level mixed-integer programming problem for solution. This ensures that the final frequency regulation strategy satisfies both the overall economic and inertia stability requirements of the microgrid, while also taking into account the individual revenue of each distributed energy node.

[0038] In step S160, frequency regulation control is performed on the microgrid based on the power grid frequency regulation optimization model to determine the inertia provided by each node.

[0039] The power grid frequency regulation optimization model established through the above steps can, under the premise of ensuring the stability of system inertia, meet the optimization objectives of the microgrid and each node as much as possible, perform optimization calculations, and thus determine the inertia provided by each node, providing guidance for the frequency regulation of the microgrid.

[0040] As described above, a node frequency response model for a microgrid with multiple nodes is constructed based on the spatial distribution characteristics of frequency fluctuations. Based on this model, a first optimization model is built with the goal of minimizing the operating cost of the microgrid and the constraint of its inertia stability. Multiple second optimization models are then constructed, with the goal of maximizing the energy revenue of distributed energy sources and the constraint of reserved energy for distributed energy sources based on the inertia stability of the microgrid. Each node corresponds to one distributed energy source. Target fault scenarios for each node are selected based on historical node operating data. Nested optimization is then performed using the first and multiple second optimization models based on the target fault scenarios for each node to obtain the microgrid frequency regulation optimization model. Finally, frequency regulation control of the microgrid is performed based on the microgrid frequency regulation optimization model to determine the inertia provided by each node. Subsequent optimization based on the node-level frequency response model solves the problem of potential local inertia loss in the microgrid, resulting in higher control accuracy and better safety. By constructing a microgrid frequency regulation optimization model, we can accurately guide the rational distribution of inertia resources in the power grid, ensuring that the frequency fluctuations of each node in the microgrid are limited to a safe range. This provides frequency regulation support when the power grid frequency fluctuates, mitigates the negative impact of inertia decline, and improves the safety and stability of power system operation.

[0041] In some embodiments of this disclosure, the spatial distribution characteristics of frequency fluctuations include: the frequency response of the microgrid is achieved by the power supply nodes adjusting their inertia, while the load nodes are passively affected by the adjustments made by the power supply nodes. Since the power supply nodes can directly adjust their frequency response by changing the inertia they provide, while the load nodes can only be passively affected by the adjustments made by the power supply nodes, it is necessary to analyze the power supply nodes and load nodes separately when constructing the node frequency response model.

[0042] In some exemplary embodiments of this disclosure, the implementation process of step S110 is as follows: Figure 2 As shown, it includes the following steps: In step S210, node frequency response equations are constructed based on the frequency responses of load nodes and power supply nodes.

[0043] In step S220, the limiting conditions for the safety performance index are determined with the objective that the impact of the microgrid's inertia configuration on the microgrid's frequency security is limited to a safe range.

[0044] In step S230, the node frequency response model of the microgrid is obtained by combining the node frequency response equation and the constraints of the safety performance index.

[0045] It should be noted that the safety performance indicators include at least: load node frequency change rate, load node frequency minimum point, power node frequency change rate, and power node frequency minimum point.

[0046] In some exemplary embodiments of this disclosure, consider a N The power grid of the node, including N G Each power node, N L Each load node. The power node equipment can autonomously control its own frequency, satisfying the synchronous generator swing equation as shown in equation (1).

[0047] (1) in, The reference angular frequency; Represents a node i Phase angle of the power supply equipment; Represents a node i The angular frequency of the power supply equipment; Represents a node i The mechanical power input to the power supply equipment; Represents a node i The electromagnetic power output by the power supply equipment; Represents a node i The inertial time constant or VSG virtual inertia of the rotor of the power supply equipment; Representing nodes respectively i Damping coefficient of the power supply equipment; Represents a node i The first derivative of the angular frequency of the power supply device with respect to time.

[0048] Since power supply nodes can directly adjust their frequency response by changing their own inertia, while load nodes can only be passively affected by the adjustment of power supply nodes, it is necessary to analyze power supply nodes and load nodes separately. Therefore, when considering the network topology of the power grid, the node susceptance matrix needs to be rearranged according to the principle of separating power supply and load nodes, as shown in equation (2).

[0049] (2) in, , representing the nodal susceptance matrix of the microgrid. , represents the node susceptibility matrix between power nodes; , represents the node susceptance matrix between the power supply node and the load node; , represents the node susceptance matrix between load nodes and power supply nodes; , represents the nodal susceptance submatrix between load nodes; that is, the first part of the nodal susceptance matrix. N G line and front N GThe column represents the power node, followed by N L Line and after N L The columns represent load nodes. Since the nodal susceptance matrix is ​​a symmetric matrix, ... .

[0050] To facilitate subsequent optimization, the line power loss is ignored, and a DC power flow model is adopted. The electromagnetic power output of the power node (i.e. the power injected into the node) can be obtained as shown in equation (3).

[0051] (3) in, The electromagnetic power vector of the power node; is the phase angle vector of the power supply node; This is the phase angle vector of the load node.

[0052] Similarly, the load node power can be obtained as shown in equation (4).

[0053] (4) in, This represents the power vector of the load node.

[0054] Since there are multiple power generation nodes in the power grid, and only power generation nodes can adjust their frequency response by adjusting their inertia, it is necessary to equivalently distribute power disturbances occurring at load nodes to power generation nodes. Therefore, it is necessary to... Use containing , The nodal susceptance matrix is ​​represented as shown in equation (5).

[0055] (5) in, , Substituting it into equation (1) and writing it in matrix form, we get: (6) in, Represents the angular frequency vector of the power supply node; express The first derivative with respect to time; Represents the mechanical power vector input to the power node; Represents the inertial time constant or VSG virtual inertia vector of the rotor of the power node; These represent the damping coefficient vectors of the power supply nodes; express The first derivative with respect to time.

[0056] The frequency of load nodes can be considered as the superposition of the frequencies of power supply nodes with respect to the network topology: (7) in, ; This represents the angular frequency vector of the load node.

[0057] In some exemplary embodiments of this disclosure, when constructing the first optimization model, different inertial excitations are applied to different nodes in the microgrid to calculate the operating cost of the microgrid. The implementation process of step S120 is as follows: Figure 3 As shown, it includes the following steps: In step S310, based on the frequency regulation recovery characteristics of the load node after power disturbance and the inertia stability requirements of the microgrid, constraints are established for the load node frequency change rate, the load node frequency minimum point, the power node frequency change rate, and the power node frequency minimum point.

[0058] It should be noted that the Rate of Change of Frequency (RoCoF) reflects the speed at which the frequency drops after a power disturbance. An excessively rapid rate of change of frequency can trigger malfunctions in protection devices. The lowest frequency point of the load node must not fall below the grid's safety lower limit; otherwise, under-frequency load shedding will be triggered. The constraint on the rate of change of power node frequency limits the frequency response speed of the power node, preventing power oscillations caused by overreaction, while ensuring the effectiveness of virtual inertia injection. The constraint on the lowest frequency point of the power node means that during frequency regulation, the power node must ensure that its local frequency does not fall below the threshold required for normal equipment operation, while also ensuring that the power node does not shut down itself while providing frequency regulation services.

[0059] It should be noted that when a power disturbance occurs at a node in a microgrid, such as a sudden increase in load or power source disconnection, the power system frequency will go through the following process: normal operation, frequency drop (RoCoF stage), reaching the lowest frequency point, and frequency regulation recovery. This dynamic process needs to be constrained to ensure that the frequency deviation does not exceed the safe range.

[0060] In step S320, inertial excitation constraints are established based on the inertial excitation limits of each node.

[0061] It should be noted that the ability of each node in a microgrid, especially distributed energy sources connected through power electronic converters, to provide virtual inertia is limited by both the physical energy storage capacity and the rated power of the power converter, and inertial excitation boundary constraints must be established.

[0062] In step S330, power constraints are established based on the power balance of the microgrid.

[0063] Understandably, based on the principle of energy conservation, a microgrid must ensure that the power generation is equal to the power consumption plus losses at any given time, and under frequency regulation control, the power output of each node must meet the overall energy balance.

[0064] The various constraints mentioned above collectively ensure the safety, feasibility, and physical rationality of the solution results from the first optimization model.

[0065] In some embodiments of this disclosure, a series of key performance indicators can be defined to reflect the dynamic behavior of the system under disturbances, so as to effectively evaluate the impact of inertia configuration on the frequency security of the system and serve as constraints for subsequent optimization models.

[0066] Taking load disturbance as an example, according to the power node swing equation (6), the change in mechanical power and phase angle of the power supply at the instant of the disturbance can be ignored. Taking the derivative with respect to time, the power node RoCoF can be obtained as follows: (8) in, , representing the power disturbance vector of the load node.

[0067] Ignoring the effects of frequency variations on network, load, and generator parameters, the derivative of equation (7) with respect to time yields: (9) As can be seen from equation (9), the load node RoCoF is related to the network topology and the power node RoCoF.

[0068] Combining equations (8) and (9), we can obtain: (10) After a power disturbance, the power system maintains its frequency at the reference value, which is mainly ensured by primary frequency regulation. Assuming that each power node has a constant rate of primary frequency regulation capability and ignoring the damping effect, equation (11) can be obtained from the synchronous generator swing equation.

[0069] (11) in, It is the primary frequency modulation rate vector of the power node, which is... N gen Dimensional column vector.

[0070] By solving the above differential equation, we can obtain: (12) in, It is the frequency vector of the primary frequency modulation dead zone of the power node, which is N gen Dimensional column vector.

[0071] The lowest point of power node frequency can be obtained. for: (13) in, yes N gen 1D unit vector.

[0072] Similarly, substituting equation (12) into equation (7), for the load node, we have: (14) The lowest frequency point of the load node can be obtained. for: (15) in, yes N load 1D unit vector.

[0073] It is understandable that the maximum value of RoCoF occurs at the instant t=0, so limiting the RoCoF at t=0 will certainly limit the RoCoF at any other time. Therefore, to maintain the stability of the power system, there is a constraint on the upper limit of the maximum value of RoCoF at load nodes.

[0074] The constraints for the load node RoCoF are: (16) in, Indicates time t The load fluctuation vector.

[0075] The constraint condition for the load node with the lowest frequency is: (17) The constraints for the power node RoCoF are: (18) The constraint condition for the lowest power node frequency is: (19) Based on the inertial excitation constraints of each node, the inertial excitation constraints are established as follows: (20) (twenty one) in, Indicates the power node at time t Inertial excitation, Indicates the load node at time [time]. t Inertial excitation.

[0076] Based on the power balance of the microgrid, the power constraint conditions are established as follows: (twenty two) in, It is a moment t Electricity consumed by load nodes.

[0077] With the goal of minimizing the operating cost of the microgrid, the first optimization model is constructed as follows: (twenty three) in, For a moment t Power Node i Provided inertia , For a moment t Power Node i The amount of electricity provided , It is a moment t Data on the value of electricity purchased or sold by microgrid operators from the point of common connection; For a moment t Inertia purchased or sold by public junctions. For a moment t Electricity purchased / sold from the common junction point. Defines a set of 24 times per day. .

[0078] It should be noted that microgrids have the ability to optimize the management of internal resources. As the upper layer, the microgrid operator provides subsidies and incentives to the lower layer distributed resources based on the price data of different nodes where they are located. That is, the upper layer provides the lower layer with the inertia price and electricity price that it is willing to purchase. The lower layer then feeds back the electricity and inertia price data of its own node to the upper layer, and provides the electricity and inertia that it is willing to provide. If there is still a demand gap in electricity and inertia, the microgrid operator will purchase electricity and inertia from the common connection point at the current wholesale market price.

[0079] In some exemplary embodiments of this disclosure, the constraints of the first optimization model may include formulas (16)-(22) shown above, and may also include formula (24) to ensure that the line capacity is not overloaded. The established upper and lower limits of power flow constraints are: (twenty four) in, It is a moment t Power output vector; It is the power of the load node; It is the vector of the maximum power flow capacity of the line.

[0080] In some exemplary embodiments of this disclosure, when step S130 is implemented, the second optimization model is used to determine the operating parameters for each distributed energy source to participate in energy dispatch under the condition of meeting the inertia stability requirements of the microgrid. Specifically, based on the node frequency response model, the frequency change characteristics of each node when the microgrid is subjected to power disturbances are determined, and the energy that the corresponding distributed energy source needs to reserve is determined according to the response capability of the node where each distributed energy source is located to frequency changes.

[0081] In practical implementation, when the equivalent inertia of the microgrid itself cannot meet the frequency change rate requirement, distributed energy sources provide rapid power support through reserved energy. The reserved energy constraints for distributed energy sources are constructed based on the inertia stability of the microgrid, including: (25) in, Indicates power node i The j The power consumption of a distributed resource; Indicates power node i The j One frequency regulation reserved capacity for distributed resources; Indicates power node i The j Inertia reserved capacity for distributed resources , Indicates power node i The j The inertia of a distributed resource; Indicates power node i The j The power limit of a distributed resource.

[0082] The second optimization model, with the goal of maximizing the energy revenue of distributed energy sources, is as follows: (26) in, Indicates power node i The inertia excitation value; Indicates power node i The incentive value of electricity; and Indicates power node i The j The cost factor for providing inertia to a distributed resource; and Indicates power node i The j The cost factor for providing electricity to a distributed resource.

[0083] The above model constraints may also include: Power output is subject to upper and lower limits: (27) in, It is a power node i The j The lower limit of power consumption for each distributed resource.

[0084] Similar to electrical charge, the inertia provided by a power source also has certain upper and lower limits. (28) in, It is a power node i The j Lower bound of inertia for distributed resources. It is a power node i The j The upper limit of inertia for a distributed resource.

[0085] In maximizing energy gains, the second optimization model does not allocate all available energy from distributed energy sources to energy trading. Instead, it pre-reserves energy and power margins for rapid frequency support based on the microgrid's inertia level, predicted power disturbances, and node frequency response characteristics, thus balancing the energy gains of distributed energy sources with the frequency stability of the microgrid. For different distributed energy sources, different microgrid nodes, different candidate operating scenarios, or different scheduling periods, corresponding node frequency response parameters, equipment operating parameters, and energy trading parameters are used to construct the second optimization model, resulting in multiple second optimization models.

[0086] It should be noted that the actual operating environment of power systems is complex and variable, and the location of disturbance sources exhibits a high degree of randomness and spatial uncertainty. This disclosure significantly improves the disturbance resilience of the configuration scheme by introducing a set of fault scenarios to cover potential disturbance risks.

[0087] In some exemplary embodiments of this disclosure, load fluctuations in actual power system operation are random and uncertain, making it difficult to precisely predict which nodes will experience load changes at a given moment. Therefore, when conducting risk analysis or optimization decisions, it is typically necessary to construct a set of node failure scenarios representing various possible operating conditions to characterize the system's operating state under different combinations of load fluctuations. For a power system containing N nodes, each node is either at a normal load level or experiencing load fluctuations during a given period; therefore, theoretically, the number of possible scenarios is 2^N. N If the baseline condition where all nodes are in normal condition is excluded, then there are at most 2 types; NThere are several load fluctuation scenarios. However, as the number of nodes N increases, the number of scenarios will grow exponentially. Directly modeling and calculating all scenarios would lead to an excessive computational burden, making it difficult to apply in practical engineering. Therefore, it is necessary to combine historical data and statistical characteristics to evaluate the probability of various scenarios, select typical scenarios with high probability of occurrence and significant impact on system operation as target failure scenarios, and ignore scenarios with extremely low probability of occurrence and minor impact. This significantly reduces the number of scenarios while ensuring analytical accuracy, thereby improving model solution efficiency and practical operability.

[0088] Specifically, when step S140 is implemented, let the set of all fault scenarios be . It can be represented as: (29) in, Indicates the first l The probability of a fault scenario can be obtained through statistical analysis of historical fault data. To facilitate scenario selection, it is required here that... To sort the scenarios in ascending order of probability, it is necessary to ensure that the sum of the probabilities of all scenarios is 1, i.e., satisfying: (30) in, Representing a scene The probability of occurrence.

[0089] Generally, power systems typically require the system to maintain stability with a certain probability, and scenarios need to be selected in ascending order of probability. So, assuming the requirement is to ensure the node frequency response is within a certain range... If the probability is such that the limit is not exceeded, then the scenarios that need to be filtered out are... , , ..., The following conditions must be met: (31) in, For a given security threshold.

[0090] Accordingly, the obtained results need to satisfy the node frequency response in The set of scenarios in which the limit will not be exceeded under any probability is: (32) in, ensure The set of fault scenarios that need to be considered without exceeding the limit under a certain probability is the target fault scenario set.

[0091] In some exemplary embodiments of this disclosure, a first optimization model serves as an outer optimization model to determine the frequency regulation resource allocation scheme of the microgrid, while multiple second optimization models serve as inner optimization models to evaluate the microgrid's frequency dynamic response when different nodes experience target faults. The solution results of the inner optimization models are fed back to the first optimization model to correct the frequency security constraints in the outer optimization models. Through alternating iterations of outer-layer decision-making and inner-layer verification, the final frequency regulation scheme simultaneously satisfies both frequency regulation economic requirements and frequency security requirements under different fault scenarios.

[0092] In specific implementation, the process of step S150 is as follows: Figure 4 As shown, the steps may include the following: In step S410, the distribution probability of power disturbance sources is determined based on the target fault scenario for each node.

[0093] It should be noted that each power source node and load node in the microgrid is considered as a candidate power disturbance source. For any node, based on the target fault scenario corresponding to that node, the probability of power disturbance events such as power surge, power drop, load connection, load disconnection, power source disconnection, or output fluctuation is determined, and the probability distribution of that node as a power disturbance source is calculated accordingly.

[0094] In one embodiment of this disclosure, the historical occurrence frequency or predicted occurrence probability of each target fault scenario is normalized to obtain the distribution probability corresponding to each candidate power disturbance source. The sum of the distribution probabilities of each candidate power disturbance source is 1. The larger the distribution probability, the higher the probability that the corresponding node is likely to become a power disturbance source during the running period to be optimized.

[0095] In step S420, based on the distribution probability of the power disturbance source, the constraints on the load node frequency change rate, the load node frequency minimum point, the power node frequency change rate, and the power node frequency minimum point are corrected.

[0096] It should be noted that the constraints for the corrected load node frequency change rate, the constraint for the load node frequency minimum point, the constraint for the power supply node frequency change rate, and the constraint for the power supply node frequency minimum point are as follows: (33) in, Representing a scene k Mid-moment t The load fluctuation vector.

[0097] (34) (35) (36) In practice, the corrected load node frequency change rate constraints, load node frequency minimum point constraints, power node frequency change rate constraints, and power node frequency minimum point constraints are fed back to the first optimization model. The first optimization model updates the frequency regulation resource allocation scheme accordingly and calls multiple second optimization models again for dynamic frequency verification. When the objective function of the first optimization model reaches the convergence condition, and each node satisfies the corrected frequency safety constraints under the corresponding target fault scenario, the nested optimization ends, and the first optimization model at this point and its associated multiple second optimization models are determined as the microgrid frequency regulation optimization model.

[0098] The embodiments of this disclosure, through the above processing, can simultaneously consider the uncertain spatial distribution of power disturbance sources, the frequency response differences of different types of nodes, and the impact of different fault scenarios on the frequency security of microgrids, so that the obtained frequency regulation optimization results match the actual fault risks of microgrids and improve the adaptability and reliability of frequency regulation schemes in multi-node, multi-fault scenarios.

[0099] In some exemplary embodiments of this disclosure, the implementation process of step S160 is as follows: Figure 5 As shown, it includes the following steps: In step S510, based on the derivation of the mapping relationship between the frequency indices of load nodes and power supply nodes, the nonlinear constraints of the power grid frequency regulation optimization model are relaxed and linearized to obtain a simplified power grid frequency regulation optimization model.

[0100] It should be noted that the simplified power grid frequency regulation optimization model is a single-level mixed integer quadratic programming model. Linearization is then performed on the model. Because... The sum of all the rows is 1, and the frequency of the load node is the weighted sum of the frequencies of the power supply nodes.

[0101] In step S520, the simplified power grid frequency regulation optimization model is solved to obtain the frequency regulation control strategy of the microgrid.

[0102] In some embodiments of this disclosure, the power grid frequency regulation optimization model is a two-level problem optimization model, which includes an upper-level problem and a lower-level problem. Based on the derivation of the mapping relationship between the frequency indices of load nodes and power generation nodes, the nonlinear constraints of the power grid frequency regulation optimization model are relaxed and linearized to obtain a simplified power grid frequency regulation optimization model. This includes: relaxing the nonlinear constraints of the frequency indices of load nodes based on the mapping relationship derivation, and linearizing the upper-level problem; adding the KKT conditions of the lower-level problem to the constraints of the linearized upper-level problem to obtain the simplified power grid frequency regulation optimization model. This can reduce the difficulty of solving the model and, while taking into account the solution efficiency, match the inertia provided by each node with the node location, power disturbance risk, and frequency security requirements.

[0103] The power grid frequency regulation optimization model obtained through nested optimization is transformed by equivalent or approximate transformations. This transforms the frequency dynamic nonlinear constraints and the two-layer optimization structure, which are difficult to solve directly, into a single-layer mixed-integer quadratic programming model that can be processed by the optimization solver. The single-layer mixed-integer quadratic programming model is then solved to obtain the frequency regulation control parameters for each node. These parameters include at least the inertia configuration of each node, and may also include one or more of the following: primary frequency regulation reserve capacity, frequency regulation power, response time, droop coefficient, and energy storage charging / discharging power.

[0104] The inertia provided by the power supply node can include the physical inertia provided by the synchronous generator, as well as the equivalent inertia provided by the inverter-type power supply, energy storage device, or controllable power supply through virtual inertia control; the inertia provided by the load node can include the equivalent inertia provided by the controllable load, load-side energy storage device, or power electronic interface equipment. For nodes that do not have inertia response capability, their inertia configuration can be set to zero.

[0105] In practice, for equation (35), multiply both sides of the inequality by the left side simultaneously. Linearization yields: (37) For equation (36), both sides of the inequality need to be multiplied on the right. Linearization yields: (38) For equation (33), similar to equation (35), the left side of the inequality sign only adds the left multiplication. It is precisely because The existence of this inequality means that it cannot be directly solved by multiplying both sides of the inequality by the left side. Linearization is performed in a manner that is based on... The definition is to multiply both sides by the left. We can obtain: .

[0106] Based on the definition of the nodal admittance matrix, we can obtain and If the sum of all elements in each row is 0, rearranging the terms yields: .Will Expanding and summing each row, we get: Combining the two equations, we can obtain: .

[0107] By changing the order of the summation signs and rearranging and combining like terms in the above expression, we can obtain: (39) Equation (39) can be written in matrix form as follows: .because It is reversible; expanding it yields: .

[0108] It can be seen that the matrix The sum of each row is 1. Considering the load node RoCoF and the power node RoCoF, we can obtain: Therefore, it can be seen that the load node RoCoF is a weighted average of the power node RoCoF. When the power node RoCoF satisfies constraint (35), the load node RoCoF must also satisfy constraint (33). Therefore, constraint (33) can be ignored in the model. Similarly, it can be obtained that the lowest frequency point of the load node is a weighted average of the lowest frequency points of the power node.

[0109] In some embodiments of this disclosure, a simplified overall single-layer model is obtained, which can be directly solved using the YALMIP+Gurobi solver: (40) in, , , , , , , , These are the corresponding Lagrange multipliers.

[0110] By solving the above model, the optimal frequency regulation task allocation scheme for various adjustable resources within the microgrid can be determined, forming a multi-timescale frequency regulation control strategy covering: frequency regulation capacity configuration, response timing arrangement, power command issuance, and frequency recovery control. The resulting microgrid frequency regulation control strategy can include, for example, the frequency regulation capacity and reserve capacity undertaken by resources such as energy storage systems, conventional generating units, and adjustable loads, clarifying the participation degree of each resource after a frequency disturbance occurs; based on the response speed and continuous adjustment capability of different resources, a phased power response scheme is formed, where virtual inertia and fast frequency control resources quickly suppress the rate of frequency change in the early stages of the disturbance, energy storage and fast adjustable loads further reduce the frequency deviation and improve the frequency minimum point, and then conventional generating units or continuously adjustable resources take over to achieve frequency recovery and power rebalancing; the active power adjustment, start-stop status, charging and discharging status, load reduction, and recovery process of each frequency regulation resource under different operating periods, different load levels, and different disturbance scenarios are also considered. This ensures that the frequency change rate, minimum frequency point, and steady-state frequency deviation meet safety requirements, while reducing frequency regulation costs, energy storage losses, and the impact of production load adjustment.

[0111] Understandably, especially in power systems with large-scale integration of renewable energy, the lack of inertia support directly weakens the power system's ability to withstand sudden disturbances, causing the grid frequency to change faster and deviate more significantly when faced with large load fluctuations or renewable energy output fluctuations, thus significantly increasing the risk of frequency instability.

[0112] This disclosure optimizes microgrids to achieve coordinated configuration of power dispatch and inertia support for multiple distributed energy sources within the microgrid, thereby maximizing resource utilization while ensuring frequency security. This disclosure constructs a joint power-inertia two-layer optimization model based on node frequency response constraints. It aims to leverage the clustered control advantages of microgrids over distributed resources to achieve higher overall energy utilization within the microgrid while meeting grid security and stability constraints. This not only effectively optimizes the microgrid's inertia configuration but also significantly improves the grid's frequency stability.

[0113] This disclosure proposes an improved power-inertia joint optimization configuration method that considers the uncertainty of fault nodes. In view of the high randomness of the location of disturbance sources in actual operation, a set of target fault scenarios for nodes is constructed. To solve the problem of difficulty in solving the model after considering uncertainty, the mapping relationship between the frequency indices of load nodes and power supply nodes is derived, and the nonlinear constraints are relaxed and linearized to construct an improved single-layer mixed integer quadratic programming model. This significantly reduces the computation time while ensuring the robustness of the power system in the face of sudden disturbances at any node.

[0114] Figure 6 This is a block diagram illustrating a distributed frequency modulation device according to some exemplary embodiments of the present disclosure.

[0115] Reference Figure 6 The device 600 includes: a node frequency response model construction unit 601, a first optimization model construction unit 602, a second optimization model construction unit 603, a fault scenario screening unit 604, a frequency modulation optimization model construction unit 605, and a frequency modulation control unit 606.

[0116] Among them, the node frequency response model construction unit 601 is used to construct the node frequency response model of the microgrid based on the spatial distribution characteristics of frequency fluctuations in the microgrid; the microgrid includes multiple nodes; The first optimization model construction unit 602 is used to construct the first optimization model based on the node frequency response model, with the minimum operating cost of the microgrid as the optimization objective and the inertial stability of the microgrid as the constraint condition. The second optimization model construction unit 603 is used to construct multiple second optimization models based on the node frequency response model, with the optimization objective of maximizing the energy revenue of distributed energy, and based on the inertial stability of the microgrid to construct the reserved energy constraints of distributed energy; wherein, each node corresponds to one distributed energy. The fault scenario filtering unit 604 is used to filter out the target fault scenarios for each node based on the node's historical operating status. The frequency regulation optimization model construction unit 605 is used to perform nested optimization based on the target fault scenario of each node, combining the first optimization model and multiple second optimization models, to obtain the microgrid frequency regulation optimization model; The frequency control unit 606 is used to perform frequency control on the microgrid based on the power grid frequency regulation optimization model and to determine the inertia provided by each node.

[0117] It should be noted that multiple nodes include at least one load node and at least one power source node. The spatial distribution characteristics of frequency fluctuations include: the microgrid's frequency response is achieved by the power source node adjusting its inertia, while the load node is passively affected by the power source node's adjustments.

[0118] In some exemplary embodiments of this disclosure, the node frequency response model construction unit 601 is configured to: construct node frequency response equations based on the frequency responses of load nodes and power supply nodes; determine the constraints of safety performance indicators with the objective that the impact of microgrid inertia configuration on microgrid frequency security is limited to a safe range; and obtain the node frequency response model of the microgrid by combining the node frequency response equations and the constraints of safety performance indicators.

[0119] In some exemplary embodiments of this disclosure, the first optimization model construction unit 602 is configured as follows: Based on the frequency regulation recovery characteristics of load nodes after power disturbances and the inertia stability requirements of microgrids, constraints are established for the frequency change rate of load nodes, the minimum frequency point of load nodes, the frequency change rate of power supply nodes, and the minimum frequency point of power supply nodes. Inertial excitation constraints are established based on the inertial excitation limit of each node. Electricity constraints are established based on the power balance of the microgrid.

[0120] In some exemplary embodiments of this disclosure, the frequency modulation optimization model construction unit 605 is configured as follows: Based on the target fault scenario for each node, determine the distribution probability of the power disturbance source; Based on the distribution probability of power disturbance sources, the constraints for correcting the load node frequency change rate, the constraint for the load node frequency minimum point, the constraint for the power node frequency change rate, and the constraint for the power node frequency minimum point are presented.

[0121] In some exemplary embodiments of this disclosure, the frequency modulation control unit 606 is configured to: Based on the derivation of the mapping relationship between the frequency indices of load nodes and power generation nodes, the nonlinear constraints of the power grid frequency regulation optimization model are relaxed and linearized to obtain a simplified power grid frequency regulation optimization model. The simplified power grid frequency regulation optimization model is solved to obtain the frequency regulation control strategy of the microgrid.

[0122] Having described the methods and apparatus of exemplary embodiments of this disclosure, the following references are made. Figure 7 A computer-readable storage medium according to exemplary embodiments of this disclosure will be described. This computer-readable storage medium can be disposed in a control device. Please refer to... Figure 7 The computer-readable storage medium shown is an optical disc 110, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it implements the steps described in the above method implementation. For example, based on the spatial distribution characteristics of frequency fluctuations in the microgrid, a node frequency response model of the microgrid is constructed; the microgrid includes multiple nodes; based on the node frequency response model, with the minimum operating cost of the microgrid as the optimization objective and the inertia stability of the microgrid as the constraint, a first optimization model is constructed; based on the node frequency response model, with the maximum energy revenue of distributed energy as the optimization objective and the reserved energy constraint of distributed energy is constructed based on the inertia stability of the microgrid, multiple second optimization models are constructed; each node corresponds to one distributed energy source; based on the historical operating conditions of the nodes, the target fault scenarios of each node are selected; based on the target fault scenarios of each node, nested optimization is performed by combining the first optimization model and multiple second optimization models to obtain the microgrid frequency regulation optimization model; based on the microgrid frequency regulation optimization model, frequency regulation control is performed on the microgrid to determine the inertia provided by each node. The specific implementation methods of each step will not be repeated here.

[0123] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0124] Having described the apparatus, method, medium, and device of exemplary embodiments of this disclosure, the following references are made... Figure 8 In the exemplary embodiments of this disclosure, the computing device for frequency modulation may be located within a control device.

[0125] Figure 8 A block diagram is shown of an exemplary computing device 120 suitable for implementing embodiments of the present disclosure, which may be a computer system or a server. Figure 8 The computing device 120 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0126] like Figure 8 As shown, the components of computing device 120 may include, but are not limited to: one or more processors or processing units 1201, system memory 1202, and bus 1203 connecting different system components (including system memory 1202 and processing unit 1201).

[0127] The computing device 120 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 120, including volatile and non-volatile media, removable and non-removable media.

[0128] System memory 1202 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 12021 and / or cache memory 12022. Computing device 120 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 12023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 8 Not shown in the image (usually referred to as a "hard drive"). Although not shown in Figure 8 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 1203 via one or more data media interfaces. System memory 1202 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0129] A program / utility 12025 having a set (at least one) of program modules 12024 may be stored, for example, in system memory 1202, and such program modules 12024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 12024 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0130] The computing device 120 can also communicate with one or more external devices 1204 (such as a keyboard, pointing device, display, etc.). This communication can be performed via input / output (I / O) interface 1205. Furthermore, the computing device 120 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 1206. Figure 8 As shown, network adapter 1206 communicates with other modules of computing device 120 (such as processing unit 1201) via bus 1203. It should be understood that, although... Figure 8 As not shown, it can be used in conjunction with computing device 120 with other hardware and / or software modules.

[0131] The processing unit 1201 executes various functional applications and data processing by running programs stored in the system memory 1202. For example, based on the spatial distribution characteristics of frequency fluctuations in the microgrid, a node frequency response model of the microgrid is constructed; the microgrid includes multiple nodes; based on the node frequency response model, a first optimization model is constructed with the minimum operating cost of the microgrid as the optimization objective and the inertia stability of the microgrid as the constraint; based on the node frequency response model, a second optimization model is constructed with the maximum energy revenue of distributed energy sources as the optimization objective and the reserved energy constraint of distributed energy sources is constructed based on the inertia stability of the microgrid; each node corresponds to a distributed energy source; based on the historical operating conditions of the nodes, target fault scenarios for each node are selected; based on the target fault scenarios of each node, nested optimization is performed by combining the first optimization model and multiple second optimization models to obtain the microgrid frequency regulation optimization model; based on the microgrid frequency regulation optimization model, frequency regulation control is performed on the microgrid to determine the inertia provided by each node. The specific implementation methods of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the distributed frequency modulation device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0132] In particular, according to embodiments of this disclosure, the process described above with reference to the flowchart can be implemented as a computer program product, which includes a computer program that, when executed by a processor, implements the above-described distributed frequency modulation method.

[0133] In the description of this disclosure, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0134] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0135] In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

[0140] Furthermore, although the operations of the methods disclosed herein are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

Claims

1. A distributed frequency modulation method, characterized in that, include: Based on the spatial distribution characteristics of frequency fluctuations in a microgrid, a node frequency response model for the microgrid is constructed; the microgrid includes multiple nodes. Based on the node frequency response model, with the minimum operating cost of the microgrid as the optimization objective and the inertial stability of the microgrid as the constraint, a first optimization model is constructed. Based on the node frequency response model, with the optimization objective of maximizing the energy revenue of distributed energy sources, and based on the inertia stability of the microgrid, the reserved energy constraints of the distributed energy sources are constructed, and multiple second optimization models are built; wherein, each node corresponds to one distributed energy source. Based on the historical operation data of the nodes, the target failure scenarios for each node are selected. Based on the target fault scenario of each node, nested optimization is performed by combining the first optimization model and the multiple second optimization models to obtain the microgrid frequency regulation optimization model; Based on the power grid frequency regulation optimization model, frequency regulation control is performed on the microgrid to determine the inertia provided by each node.

2. The distributed frequency modulation method according to claim 1, characterized in that, The plurality of nodes includes: at least one load node and at least one power node; Based on the aforementioned power grid frequency regulation optimization model, frequency regulation control is performed on the microgrid, including: Based on the derivation of the mapping relationship between the frequency indices of the load nodes and the power supply nodes, the nonlinear constraints of the power grid frequency regulation optimization model are relaxed and linearized to obtain a simplified power grid frequency regulation optimization model; wherein, the simplified power grid frequency regulation optimization model is a single-layer mixed integer quadratic programming model. The simplified power grid frequency regulation optimization model is solved to obtain the frequency regulation control strategy of the microgrid.

3. The distributed frequency modulation method according to claim 2, characterized in that, The power grid frequency regulation optimization model is a two-level problem optimization model, which includes an upper-level problem and a lower-level problem; Specifically, based on the mapping relationship between the frequency indices of the load nodes and the power supply nodes, the nonlinear constraints of the power grid frequency regulation optimization model are relaxed and linearized to obtain a simplified power grid frequency regulation optimization model, including: Based on the mapping relationship, the nonlinear constraints of the frequency index of the load node are relaxed, and the upper-level problem is linearized. By adding the KKT conditions of the lower-level problem to the constraints of the upper-level problem after linearization, a simplified power grid frequency regulation optimization model is obtained.

4. The distributed frequency modulation method according to claim 2, characterized in that, The frequency fluctuation spatial distribution characteristics include: the frequency response of the microgrid is achieved by the power node through the adjustment of inertia, and the load node is passively affected by the adjustment of the power node; Based on the spatial distribution characteristics of frequency fluctuations in a microgrid, a node frequency response model for the microgrid is constructed, including: Based on the frequency responses of the load node and the power node, a node frequency response equation is constructed. With the goal of limiting the impact of the microgrid's inertia configuration on the microgrid's frequency security to a safe range, the limiting conditions for the security performance indicators are determined. By combining the node frequency response equation and the constraints of the safety performance indicators, the node frequency response model of the microgrid is obtained.

5. The distributed frequency modulation method according to claim 4, characterized in that, The safety performance indicators include at least: Load node frequency change rate, load node frequency minimum point, power node frequency change rate, power node frequency minimum point.

6. The distributed frequency modulation method according to claim 5, characterized in that, Using the inertial stability of the microgrid as a constraint, a first optimization model is constructed, including: Based on the frequency regulation recovery characteristics of load nodes after power disturbance and the inertia stability requirements of the microgrid, constraints are established for the frequency change rate of the load nodes, the minimum frequency point of the load nodes, the frequency change rate of the power supply nodes, and the minimum frequency point of the power supply nodes. Based on the inertial excitation constraints of each node, establish inertial excitation constraints. Based on the power balance of the microgrid, power constraints are established.

7. The distributed frequency modulation method according to claim 6, characterized in that, Based on the target fault scenario for each node, nested optimization is performed using the first optimization model and the multiple second optimization models to obtain a microgrid frequency regulation optimization model, including: Based on the target fault scenario for each node, determine the distribution probability of the power disturbance source; Based on the distribution probability of the power disturbance source, the constraints for correcting the load node frequency change rate, the constraint for the load node frequency minimum point, the constraint for the power node frequency change rate, and the constraint for the power node frequency minimum point are determined.

8. The distributed frequency modulation method according to claim 1, characterized in that, When constructing the first optimization model, different inertia excitations are given to different nodes in the microgrid to calculate the operating cost of the microgrid.

9. A distributed frequency modulation device, characterized in that, include: A node frequency response model construction unit is used to construct a node frequency response model of the microgrid based on the spatial distribution characteristics of frequency fluctuations; the microgrid includes multiple nodes. The first optimization model construction unit is used to construct a first optimization model based on the node frequency response model, with the minimum operating cost of the microgrid as the optimization objective and the inertia stability of the microgrid as the constraint condition. The second optimization model construction unit is used to construct multiple second optimization models based on the node frequency response model, with the optimization objective of maximizing the energy revenue of distributed energy, and based on the inertia stability of the microgrid to construct the reserved energy constraints of the distributed energy; wherein each node corresponds to one distributed energy. The fault scenario filtering unit is used to filter out the target fault scenarios for each node based on the node's historical operating status. The frequency regulation optimization model construction unit is used to perform nested optimization based on the target fault scenario of each node, combining the first optimization model and the multiple second optimization models, to obtain the microgrid frequency regulation optimization model; The frequency control unit is used to perform frequency control on the microgrid based on the power grid frequency regulation optimization model and to determine the inertia provided by each node.

10. A computing device, characterized in that, The computing device includes: At least one processor, memory, and input / output unit; The memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the distributed frequency modulation method as described in any one of claims 1 to 8.

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