Continuous-time hierarchical aggregation and coordinated scheduling method for microgrid active support

Abstract

The application belongs to the technical field of micro-grid optimization, and specifically discloses a continuous-time hierarchical aggregation and collaborative scheduling method for active support of a micro-grid. Through the application, hierarchical aggregation of a decentralized aggregation unit, a micro-grid unit and a coordination layer is adopted, the upper level only uses an equivalent flexible set / active support power supply model for optimization scheduling, information interaction and unified modeling scale are significantly reduced, and the solving efficiency and implementability of collaborative scheduling are improved; through continuous-time processing, dynamic changes of variables such as power and voltage within each scheduling period are included in the model expression, intermediate overrunning and planning deviation caused by discrete constant approximation are reduced, and the flexibility and executability of scheduling instructions are improved; through consistency checking and iterative correction of the micro-grid active support power supply model, an effective instruction range that meets the internal operation constraints of the micro-grid is formed, the boundary scheduling plan solved can be decomposed into output instructions of each distributed resource in the micro-grid, and the reliability of micro-grid support operation is improved.

Description

Technical Field

[0001] This application belongs to the field of microgrid optimization technology, and more specifically, relates to a continuous-time hierarchical aggregation and collaborative scheduling method for active support of microgrids. Background Technology

[0002] With the large-scale integration of distributed resources such as distributed photovoltaics, energy storage, electric vehicles, and temperature-controlled loads on the distribution and user sides, microgrids exhibit characteristics such as diverse resource types, dispersed entities, bidirectional power interaction, and increased power electronics. Especially in the operation scenario of microgrid clusters composed of multiple microgrid units, the upper-level grid or distribution side needs to comprehensively consider the operational capabilities of tie-line power interaction, reactive power regulation, and ramp-up in collaborative optimization operations, and places higher demands on the feasibility of plans and rapid solution in disturbance or emergency scenarios.

[0003] In existing collaborative optimization schemes, a common approach is to construct a unified centralized optimization model that incorporates upper-level network constraints, microgrid cluster network constraints, and temporally coupled constraints of numerous distributed resources into a single framework for joint solution. However, in engineering applications, this centralized approach typically faces the following problems: First, resources within a microgrid cluster are dispersed and involve multiple stakeholders; operational constraints, state information, and adjustability exhibit privacy and heterogeneity, making unified, open, and accurate modeling difficult. Second, the joint model simultaneously includes network constraints and temporally coupled constraints of numerous resources, leading to rapid model expansion and significantly increased solution complexity, making it difficult to meet the computational speed requirements of rolling optimization or emergency scenarios. Third, centralized methods often rely on high data standardization and communication interaction costs, making engineering deployment and multi-stakeholder collaborative implementation challenging.

[0004] To reduce information interaction and computational complexity, collaborative operation methods based on hierarchical or decompositional approaches have emerged in recent years. For example, an equivalent operational capability description of the microgrid cluster is constructed at the tie-point level and incorporated into higher-level optimization. However, existing equivalent capability models often employ discrete-time segmented modeling, approximating power and state with a single constant variable within each scheduling period. Limited by time resolution, this makes it difficult to characterize continuous changes within the time period, easily leading to undetected transients of ramping, voltage, and energy dynamics in the middle of the period. This makes the boundary plans issued by the higher level unrealizable within the microgrid cluster or requires additional conservative margins, reducing resource utilization efficiency and operational economy. Furthermore, if the equivalent capability description lacks guarantees of realizability and decomposability, the boundary plans issued by the higher level may not be decomposed into executable instructions for each entity within the microgrid cluster, affecting the feasibility and reliability of collaborative optimization operation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application aims to provide a continuous-time hierarchical aggregation and collaborative scheduling method for active support of microgrids. This method addresses the problems of traditional centralized modeling in the collaborative optimization operation of microgrid groups, which struggles to comprehensively and accurately obtain information about each node, has excessively large models with high computational complexity, and lacks sufficient fine-grained representation capabilities for distributed resources.

[0006] To achieve the above objectives, firstly, this application provides a continuous-time hierarchical aggregation and collaborative scheduling method for active support of microgrids, including: In a microgrid cluster, each decentralized aggregation unit constructs a set of time-series feasible domains for each distributed resource unit based on the operating characteristics and constraints of each distributed resource unit within its jurisdiction. The distributed aggregation unit aggregates the time-series feasible domain set of all distributed resource units within its jurisdiction to obtain an equivalent flexible set with aggregation / deaggregation mapping relationship, which is then sent to the corresponding microgrid management unit. After receiving the equivalent flexible sets of each distributed resource cluster, the microgrid management unit constructs a microgrid operation model containing the constraints of the equivalent flexible sets, performs continuous-time processing on the time-series variables in the microgrid operation model, and obtains a solvable microgrid continuous-time operation model. The microgrid management unit aggregates the switching power capacity of the entire microgrid at the tie point into an active support power model based on the microgrid continuous-time operation model. The microgrid management unit performs consistency verification and iterative correction on the active support power source model and the microgrid continuous-time operation model, and sends the verified and corrected active support power source model to the microgrid group coordination layer. The microgrid group coordination layer incorporates the verified and corrected active support power source models of each microgrid into the coordination layer scheduling optimization model, solves for the switching power at each microgrid interconnection point, and sends it to the microgrid management unit. The microgrid management unit receives the switching power at the tie point, substitutes it as a boundary condition into the verified and corrected microgrid continuous-time operation model, solves for the power scheduling instructions corresponding to each distributed resource cluster, and sends them to the distributed aggregation unit. Based on the de-aggregation mapping relationship, the distributed aggregation unit de-aggregates the received power scheduling instructions into output instructions for each distributed resource unit and executes them.

[0007] Preferably, the distributed aggregation unit obtains a maximum inner approximation set of the Minkowski sum through affine transformation, thereby realizing the aggregation of the temporally feasible domain set of all distributed resource units within its jurisdiction: (1) Define the reference adjustable power set ,in, This indicates that distributed resources are in a scheduling cycle. Reference power at each time point within the range, This represents the coefficient matrix extracted from each constraint, with dimension 1. , The number of constraints, the average value of the boundary parameters of the power set of each dispersed aggregation unit. , These are boundary parameters, determined by the upper and lower bounds of the constraints that different distributed resource units must satisfy. This indicates the number of distributed resource units managed by a single decentralized aggregation unit; (2) Define combination Represents the set of adjustable reference power. The equivalent adjustable capacity set of distributed resources managed by the approximately aggregated decentralized aggregation unit The affine transformation parameters are defined as a combination. Represents the set of adjustable reference power. The adjustable power set of each dispersed polymerization unit after approximate polymerization Affine transformation parameters: ; (3) Solve the following optimization model to achieve the maximum inner approximation of the true Minkowski sum;

[0008] in, Representation matrix traces, Represents the dual factor. Indicates constraints; (4) Using the parameter combination obtained from the solution For the reference adjustable power set By performing an affine transformation, we obtain the equivalent adjustable capability set of distributed resources managed by the decentralized aggregation unit. .

[0009] Preferably, the microgrid operation model is as follows: The operating constraints of the micro gas turbine are: ; in, and These represent the active and reactive power outputs of the micro gas turbine, respectively. for The upper boundary, For the power-related factors of a micro gas turbine, the subscript is... Refers to the connection on the busbar superscript, subscript Indicates the corresponding time ; The charge and discharge constraints of energy storage devices include:

[0010] in, and These represent the active and reactive power of the energy storage device, respectively. The reactive power is generated by the converter. and These represent the upper boundaries of the active power for discharging and charging the energy storage device, respectively. For the power-related factors of the energy storage device, The amount of energy stored in the energy storage device. These are the upper and lower bounds of the energy stored in the energy storage device. The energy loss rate of the energy storage device. For the charging efficiency of energy storage devices, The step size between adjacent time steps; The operational constraints of distributed photovoltaic aggregation are: ; in, and These represent the active and reactive power of distributed photovoltaic power, respectively. This represents the upper boundary of the active power of distributed photovoltaic power. For distributed photovoltaic power generation, the power-related factor is _____. The operating constraints after temperature control load aggregation are: ; in, For the power of the temperature-controlled load, and These are the upper and lower bounds of the power for the temperature-controlled load, respectively. The operational constraints of the aggregated electric vehicles include:

[0011]

[0012] in, For the power of electric vehicles, and These represent the upper and lower bounds of the power output of electric vehicles. The amount of energy stored for electric vehicles These are the upper and lower bounds of the energy stored in an electric vehicle. For electric vehicle charging efficiency; Microgrid DistFlow power flow operation models that ignore line losses include:

[0013] in, and They represent Timetable The trend of meritorious and ineffective actions busbar The square of the voltage, and for upper and lower boundaries, and for upper and lower boundaries, and busbar voltage upper and lower boundaries, and Representing the lines respectively The reactance and resistance, For a set of routes, the subscript is... Indicates the busbar Starting bus The route with the destination as the destination , These respectively represent connections to the busbar The active and reactive power of the load.

[0014] Preferably, the microgrid management unit aggregates the switching power capacity of the entire microgrid at the tie point into an active support power model based on the microgrid's continuous-time operation model, as follows: ; in, This indicates a connection to a higher-level coordination layer network node. Active support power supply, and for Corresponding to upper and lower bounds, This indicates the operational constraints of the active support power supply. For the identity matrix and The matrix formed This represents the maximum infeasibility power obtained from the solution. for and The resulting matrix, i.e., the power boundary, Indicates the scheduling period.

[0015] Preferably, the microgrid management unit performs consistency verification and iterative correction on the active support power source model, as follows: (a) Construct a two-layer optimization model for consistency verification that minimizes the difference between the boundary of the active support power exchange power and the actual contact point exchange power boundary. (b) Find the dual of the bottom layer of the bi-level optimization model, and use the kkt condition to correct the product term of the variables to obtain the single-level maximization dual problem. Set the number of iterations s=1. (c) Solve the single-layer dual problem to obtain the maximum infeasible boundary exchange power. Combine the original operating constraints of the microgrid to construct a problem to minimize the difference between the boundary exchange power and the feasible boundary. Solve the problem to obtain the feasible power closest to the maximum infeasible boundary exchange power. (d) Using the operating boundary parameters of the most recent feasible power-constrained active support power model, construct a maximization problem about the power boundary of the active support power model; (e) Solve the maximization problem to obtain a new set of power boundaries for the active support power source model. Substitute these into the single-layer maximization dual problem and return to step (c), and set s=s+1. (f) Repeat steps (c)-(e) until the optimal value of the single-layer maximization dual problem is 0, and obtain the active support power source model that reflects the actual power exchange capability of the microgrid at the junction point.

[0016] Preferably, the consistency verification two-layer optimization model is as follows: ; ; in, These represent the positive and negative differences between the adjustable power of the active support power supply and the actual support power at the microgrid interconnection point, respectively. and Let a and b represent the coefficient matrices extracted from the inequalities and equality constraints in the original microgrid operation model, respectively, where a and b are the remaining constants in the constraints. This is the coefficient matrix extracted from the constraints related to boundary exchange power. This represents all variables in the microgrid operation model. This represents the equality constraints related to the power transmitted at the tie points in a microgrid. For the identity matrix and The matrix formed Indicates the scheduling period. This represents the maximum infeasibility power obtained from the solution. Indicates the first The iteration yielded , This is a matrix formed by the upper and lower bounds of the active support power supply at time t, which is connected to the network node k in the upper coordination layer.

[0017] Preferably, the single-layer maximization dual problem is as follows: ; ; in, , , , As dual variables, To represent the transpose of a matrix, Let a and b be the matrix connecting the upper and lower bounds of the active support power supply at time t, which is located at node k in the upper coordination layer network. Let a and b be the remaining constants in the constraints. and These represent the coefficient matrices extracted from the inequalities and equality constraints in the original microgrid operation model, respectively. The coefficient matrix is ​​extracted from the constraints related to boundary exchange power. For the identity matrix and The matrix formed Indicates the scheduling period. The affine transformation parameter represents the equivalent adjustable capability set of distributed resources governed by the baseline adjustable power set to the approximately aggregated distributed aggregated unit. This represents the actual aggregated set of distributed resource clusters managed by the decentralized aggregation unit. These are slack variables.

[0018] Preferably, the problem of minimizing the difference between the boundary exchange power and the feasible boundary is as follows: ; ; in, This represents the maximum infeasibility power obtained from the solution. To obtain the maximum infeasible power The most recent feasible power, The coefficient matrix is ​​extracted from the constraints related to boundary exchange power. This represents all variables in the microgrid operation model. This represents the equality constraints related to the power transmitted at the tie points in a microgrid. This represents the domain of operational variables in the microgrid operation model.

[0019] Preferably, the problem of maximizing the power boundary of the active support power source model is as follows: ; ; in, Represents the identity matrix. To represent the transpose of a matrix, Indicates the first The iteration yielded , This is a matrix formed by the upper and lower bounds of the active support power supply at time t, connected to the network node k in the upper coordination layer. As slack variables, For the identity matrix and The matrix formed Indicates the scheduling period. To find the nearest feasible power to the maximum infeasible power, This represents the matrix dot product operator.

[0020] To achieve the above objectives, in a second aspect, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the continuous-time hierarchical aggregation and cooperative scheduling method as described in the first aspect.

[0021] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0022] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: (1) Reduce the difficulty and computational complexity of collaborative modeling: This application adopts a hierarchical aggregation and solution framework of distributed aggregation unit - microgrid unit - coordination layer. The upper level only uses the equivalent flexible set / active support power source model for optimization scheduling, without needing to obtain information on all nodes and individual resources in the lower level system, thereby significantly reducing information interaction and unified modeling scale, and improving the solution efficiency and feasibility of collaborative scheduling.

[0023] (2) Improve the modeling accuracy and executability within the time period: This application incorporates the dynamic changes of variables such as power and voltage in each scheduling time period into the model expression through continuous time processing, so that the ramp, energy dynamics, voltage constraints, etc. can be more finely characterized within the time period, reducing the intermediate over-boundary and planning deviation caused by discrete constant approximation, and improving the flexibility and executability of scheduling instructions.

[0024] (3) Ensure the feasibility and decomposability of the equivalent model: This application forms an effective instruction range that satisfies the internal operating constraints of the microgrid by performing consistency verification and iterative correction on the active support power source model of the microgrid. This enables the boundary scheduling plan of the upper-level grid side to be decomposed into executable output instructions of each decentralized aggregation unit and its distributed resources within the microgrid, thereby improving the reliability of the microgrid's support operation. Attached Figure Description

[0025] Figure 1 This is a flowchart of a continuous-time hierarchical aggregation and collaborative scheduling method for active support of microgrids provided in an embodiment of this application.

[0026] Figure 2 This application provides an embodiment demonstrating the intent of implementation in a multi-level network containing a microgrid.

[0027] Figure 3 This is a schematic diagram comparing the power curve and the discrete scheduling curve under the continuous-time theory provided in the embodiments of this application, including the control curve drawn by each interpolation coefficient point.

[0028] Figure 4 This is a node system diagram provided in an embodiment of the present application, which consists of a 6-node upper-level coordinated power grid and a microgrid.

[0029] Figure 5 This is a schematic diagram of the distributed resource aggregation result on the decentralized aggregation unit side provided in the embodiments of this application.

[0030] Figure 6 This refers to the equivalent upper and lower limits of the power exchangeable at each junction point in the microgrid obtained by aggregation according to the embodiments of this application.

[0031] Figure 7 This is a schematic diagram of the active support power supply model switching power plan provided in the embodiments of this application.

[0032] Figure 8(a) shows the power command of the decomposed temperature-controlled load cluster provided in an embodiment of this application.

[0033] Figure 8(b) shows the decomposed photovoltaic cluster power command provided in an embodiment of this application.

[0034] Figure 8(c) shows the decomposed electric vehicle cluster energy storage control command provided in an embodiment of this application.

[0035] Figure 9(a) is a schematic diagram of the output power trajectory of a temperature-controlled load individual provided in the embodiments of this application.

[0036] Figure 9(b) is a schematic diagram of the output power trajectory of an individual photovoltaic device provided in the embodiments of this application.

[0037] Figure 9(c) shows the changes in electric vehicle energy storage provided in the embodiments of this application.

[0038] Figure 10 This is a schematic diagram illustrating the operation of the continuous-time hierarchical aggregation and collaborative scheduling method provided in this application embodiment in multi-level power grids. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0040] In this application, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A existing alone, A and B existing simultaneously, and B existing alone. In this application, the symbol " / " indicates that the related objects are in an "or" relationship, for example, A / B means A or B.

[0041] In this application, the terms "first" and "second," etc., are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.

[0042] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0043] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0044] The embodiments of this application are described below with reference to the accompanying drawings.

[0045] like Figure 1 As shown, this application provides a continuous-time hierarchical aggregation and cooperative scheduling method for active support of microgrids, including the following steps: S1. Modeling of feasible regions for distributed resources: Analyze the operating characteristics and constraints of each distributed resource in the microgrid group, construct a set of time-series feasible regions that can describe the power / energy changes within the scheduling cycle, and collect various types of distributed resources within the jurisdiction of the decentralized aggregation unit.

[0046] S2. Distributed resource cluster aggregation: Each decentralized aggregation unit adopts the Minkowski sum aggregation method based on affine transformation to aggregate multiple feasible sets of distributed resources within its jurisdiction into an equivalent flexible set and send it to the microgrid management unit.

[0047] S3. Microgrid operation model construction: After receiving the equivalent flexible set, each microgrid management unit constructs a microgrid operation model containing the constraints of the equivalent flexible set, and uses the solution space transformation method based on BP interpolation to perform continuous time processing on the operation model.

[0048] S4. Equivalent Operation Capabilities of Each Microgrid: Based on the microgrid operation model, the microgrid management unit equivalents the microgrid's external power exchange capability, reactive power support, and ramp-up capability to an active support power source model that can absorb / generate power at a junction point, and forms an equivalent constraint description that can be used by the coordination layer.

[0049] S5. Equivalent Model Verification and Correction: A customized iterative verification algorithm is used to verify the consistency between the active support power source model and the microgrid operation model and make necessary corrections to meet the requirements of the feasibility and decomposability of the microgrid operation, and then uploads the results to the upper-level microgrid group coordination layer.

[0050] S6. Microgrid Group Coordination Layer Scheduling: The microgrid group coordination layer incorporates the active supporting power source model of each microgrid at the interconnection point into the coordination scheduling optimization model to obtain the boundary power interaction plan at the interconnection point of each microgrid unit.

[0051] S7. Microgrid Internal Command Decomposition: Each microgrid management unit substitutes the tie point plan as a boundary condition into the microgrid continuous-time optimization model to obtain the scheduling commands corresponding to each distributed resource cluster and issues them.

[0052] S8. Decentralized De-aggregation Execution: Each decentralized aggregation unit de-aggregates the cluster scheduling instructions into resource unit-level output instructions based on the de-aggregation mapping relationship generated in step S2, and organizes their execution.

[0053] It should be noted that, as Figure 2 As shown, distributed resources (such as micro gas turbines (MT), distributed photovoltaic (DPVC), energy storage devices (ES), temperature-controlled loads (TCLC), and electric vehicle fuel cells (EVF)) are geographically dispersed and managed by different decentralized aggregation units. Based on this, regional-level integration is achieved through microgrid management units, aggregating adjacent distributed resource clusters with local loads to form independently operating microgrids, achieving local energy balance and optimization. Multiple microgrids are then electrically interconnected through a higher-level public distribution network, forming a microgrid cluster. Finally, a microgrid cluster coordination layer provides unified management at the system level, coordinating power exchange between each microgrid and the main grid, optimizing global resource allocation, and achieving the operation and stability control of the entire system.

[0054] In step S1, the process of constructing a set of high-dimensional polyhedra characterizing the feasibility of its temporal power trajectory is as follows: Each distributed aggregation unit constructs constraints that accurately describe the output limitations of the distributed resources under its jurisdiction, based on the power and energy characteristics of the distributed resources it manages, as follows: (1) (2) (3) in, This is the availability reduction factor, set to ensure the photovoltaic power grid integration rate. For a moment t No. i The power output of each photovoltaic unit. and Representing time respectively t No. i The upper and lower limits of photovoltaic power. For a moment t No. i The ambient temperature of the temperature-controlled load. For a pre-set temperature zone, For the power of the temperature-controlled load, The energy efficiency coefficient for this load is... The corresponding thermal resistance; For a moment t No. i The charging and discharging power of an electric vehicle and These are the upper and lower bounds of the power. For electric vehicles t The amount of energy stored, For the maximum capacity of electric vehicles, For charging efficiency, The step size is the step size between adjacent time steps.

[0055] Equation (1) describes the photovoltaic output power, and the photovoltaic power at each moment. There are upper and lower limits, which are specifically determined by the predicted light intensity; Equation (2) describes the change in temperature-controlled load power, and this constraint means that the heat pump or air conditioner must ensure that the indoor temperature is within the set temperature range. The specific power output is also affected by the energy efficiency coefficient. and thermal resistance The influence; Equation (3) describes the charging and discharging and energy state of electric vehicles. This constraint indicates that the charging and discharging power is within the rated upper and lower limits; the energy (SOC) is within the capacity range; and the energy is updated with the power evolution.

[0056] Regardless of the type of distributed resource, they can all be represented by the following set of power adjustable capabilities: (4) in, Indicates the first i A set of adjustable capabilities for distributed resources; It is a dimension of The vector represents the first... i Distributed resources in a scheduling cycle Power at each moment within; This represents the coefficient matrix extracted from each constraint. n The number of constraints; These are boundary parameters, determined by the upper and lower bounds of the constraints that different distributed resources need to satisfy. n The size depends on the number of constraints that need to be satisfied for a single distributed resource. Equation (4) corresponds to the power time-series feasible region of a single distributed resource.

[0057] In step S2, each decentralized aggregation unit manages the actual aggregated set of distributed resource clusters. for: (5) in, This indicates the number of distributed resources managed under a single decentralized aggregation unit / distributed resource cluster.

[0058] This approach involves calculating the Minkowski sum of the power sets of each distributed resource, which means summing up the possible power combinations for each distributed resource in turn. However, this calculation is an NP-hard problem, and it becomes difficult to solve directly as the problem size increases.

[0059] This application obtains a maximum inner approximation set of the Minkowski sum through several affine transformations. The specific steps are as follows: S21. Define the reference adjustable power set As shown in equation (6), where, This represents the average value of the boundary parameters of each volume power set; S22. Define Combinations and From the base set to the approximate aggregated set, respectively. and the set of adjustable power for each individual The affine transformation parameters are given, and the specific transformation is shown in equation (7); S23. Solve the optimization model as shown in equations (8)-(12) to achieve the maximum inner approximation of the true Minkowski sum; S24. Utilize the parameter combinations obtained from the solution. For the benchmark set By performing an affine transformation, the equivalent adjustable capability set of distributed resources managed by the decentralized aggregation unit can be obtained. .

[0060] (6) (7) (8) (9) (10) (11) (12) in, Representation matrix traces, Represents the dual factor. This indicates a constraint condition.

[0061] For the optimization model (8)-(12), equation (8) is the objective function, and the matrix is ​​required to be... The trace is maximized, thus achieving the maximum inner approximation of the true Minkowski sum. Equation (9) represents the affine transformation parameters. equal to each parameter Linear summation. Equation (10) specifies the dual factor. Greater than 0. Equation (11) is the dual condition. Equation (12) restricts the power set after each affine transformation to be included in the original power sets of each distributed resource.

[0062] In step S3, the microgrid operation model established in this application is as follows: (13) (14) (15) (16) (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) in, and These represent the active and reactive power outputs of the micro gas turbine, respectively. for The upper boundary, For the power-related factors of a micro gas turbine, the subscript is... Refers to the connection on the busbar superscript, subscript Indicates the corresponding time ; and These represent the active and reactive power of the energy storage device, respectively. The reactive power is generated by the converter. and These represent the upper boundaries of the active power for discharging and charging the energy storage device, respectively. For the power-related factors of the energy storage device, The amount of energy stored in the energy storage device. These are the upper and lower bounds of the energy stored in the energy storage device. The energy loss rate of the energy storage device. For the charging efficiency of energy storage devices, The step size between adjacent time steps; and These represent the active and reactive power of distributed photovoltaic power, respectively. This represents the upper boundary of the active power of distributed photovoltaic power. For distributed photovoltaic power generation, the power-related factor is _____. For the power of the temperature-controlled load, and These are the upper and lower bounds of the power for the temperature-controlled load, respectively. For the power of electric vehicles, and These represent the upper and lower bounds of the power output of electric vehicles. The amount of energy stored for electric vehicles These are the upper and lower bounds of the energy stored in an electric vehicle. For electric vehicle charging efficiency; and They represent Timetable The trend of meritorious and ineffective actions busbar The square of the voltage, and for upper and lower boundaries, and for upper and lower boundaries, and busbar voltage upper and lower boundaries, and Representing the lines respectively The reactance and resistance, For a set of routes, the subscript is... Indicates the busbar Starting bus The route with the destination as the destination , These respectively represent connections to the busbar The active and reactive power of the load.

[0063] Equation (13) represents the operating constraints of the micro gas turbine; Equations (14)-(16) represent the charging and discharging constraints of the energy storage device; Equations (17), (18), and (19)-(21) represent the equivalent flexible operating constraints of distributed photovoltaic, temperature-controlled load, and electric vehicle aggregation, respectively. Their expressions are basically consistent with constraints (1)-(3), but each power and upper and lower limits are equivalent aggregation values ​​after affine transformation; the above constraints are uniformly represented by symbols. To define the power-related factor; Equations (22)-(27) are the microgrid DistFlow power flow operation models that ignore line losses.

[0064] In step S3, the continuous-time processing (solution space transformation based on BP interpolation) method steps are as follows: S31. Subdivide the variables such as active power, reactive power and voltage that change with time in the operation model (Equation 13-27): As shown in Equation (28), change the power magnitude represented by a single value in a scheduling period in the original discrete time scheduling to the power magnitude represented by four interpolations. S32. A time period t Dividing the data into three equal parts yields the positions of the four interpolation coefficient points. The time interval is represented by [0,1], where any point within that interval represents a given time. τ The relationship between the power and the interpolation coefficients satisfies equation (29). The relationship between each interpolation coefficient and the power at the beginning and end can also be derived by reverse deduction, as shown in equation (30). S32. To ensure the continuity of adjacent time periods, Equation (31) is used to constrain the interpolation coefficient points of adjacent time periods; S33. Rewrite the time-related constraints in the original model into constraints that reflect the continuous changes, that is, transform the differential terms, integral terms, equality and inequality constraints in the original running model as shown in equations (32)-(35), thereby transforming the entire model into the interpolation coefficient space; S34. For ease of subsequent description and analysis, the entire operating model is expressed using the set of continuous time variables shown in equation (36).

[0065] (28) (29) (30) (31) (32) (33) (34) (35) (36) In the formula, the subscript t Generally, this corresponds to each time period in the original discrete scheduling model. The superscript B indicates that the variable consists of four interpolation coefficient values. All variables with the superscript B are vectors; This means that the power magnitude at any continuous time point can be obtained using this method; specifically, Equal to the combination number, For any continuous time; and These represent the scheduling periods. t The power at the initial and final moments of the time interval. and These represent the previous scheduling period and the next scheduling period, respectively. The sum of constants on the right-hand side of an inequality constraint; sets middle, This represents all variables in the microgrid operation model. and ...

[0066] The solution space transformation method based on BP interpolation is only one example of continuous time processing, and this application is not limited to this method.

[0067] exist Figure 3 In the diagram, we can see that the control curve is composed of the interpolation coefficient points. Based on the control curve, we can obtain the Bezier curve that represents the continuous-time power. At this point, the power curve replaces the step curve in the original scheduling.

[0068] In step S4, the active support power source model that can absorb / generate power at the connection point between each microgrid and the upper-level coordination layer can be described by the following mathematical expression: (37) in, This indicates a connection to a higher-level coordination layer network node. Active support power supply, and for Corresponding to upper and lower bounds, This indicates the operational constraints of the active support power supply. For the identity matrix and The matrix formed This represents the maximum infeasibility power obtained from the solution. for and The resulting matrix, i.e., the power boundary, Indicates the scheduling period.

[0069] The active support power supply model iterative verification algorithm method in step S5 is as follows: S51. Construct a two-layer optimization model for consistency verification as shown in equations (38)-(39) to ensure that the difference between the equivalent boundary of the active support power exchange power and the actual contact point exchange power is minimized. S52. Find the dual of the bottom layer of the bi-layer optimization model and use the kkt condition to correct the variable product term to obtain the single-layer maximization dual problem as shown in equations (40)-(41), and set the number of iterations s=1; S53. Solve the single-layer dual problem to obtain the least feasible boundary exchange power. Based on the original operating constraints of the microgrid, construct the minimization problem as shown in equations (42)-(43) to obtain the feasible power closest to the infeasible power; S54. Use this most recently feasible power to constrain the operating boundary parameters of the active support power model, i.e., construct the maximization problem as shown in equations (43)-(45); S55. Solving yields a new set of solutions. ,Will Substitute into the single-layer dual problem (40)-(41) and return to step S53, and set s=s+1; S56. Repeat steps S53-S55 until the optimal value of the single-layer dual problem is 0, and then the mathematical expression that reflects the true power exchange capability of the active support power source model can be obtained. .

[0070] (38) (39) (40) (41) (42) (43) (44) (45) in, These represent the positive and negative differences between the adjustable power of the active support power supply and the actual support power at the microgrid interconnection point, respectively. and Let a and b represent the coefficient matrices extracted from the inequalities and equality constraints in the original microgrid operation model, respectively, where a and b are the remaining constants in the constraints. The coefficient matrix is ​​extracted from the constraints related to boundary exchange power. This represents all variables in the microgrid operation model. This represents the equality constraints related to the power transmitted at the tie points in a microgrid. For the identity matrix and The matrix formed Indicates the scheduling period. This represents the maximum infeasibility power obtained from the solution. Indicates the first The iteration yielded , This is a matrix formed by the upper and lower bounds of the active support power supply at time t, connected to the network node k in the upper coordination layer. , , , As dual variables, To represent the transpose of a matrix, The affine transformation parameter represents the equivalent adjustable capability set of distributed resources governed by the baseline adjustable power set to the approximately aggregated distributed aggregated unit. This represents the actual aggregated set of distributed resource clusters managed by the decentralized aggregation unit. To obtain the maximum infeasible power The most recent feasible power, This represents the domains of operational variables in the microgrid operation model. Represents the identity matrix. This represents the matrix dot product operator. These are slack variables used to describe the kkt relaxation conditions. .

[0071] Some of the constraints in equation (40) have been described above, among which, Equations (40)-(41) are used to describe the equality constraints associated with the transmission power at the tie point in a microgrid. , , , These are dual variables.

[0072] In step S6, the objective function of the coordination layer scheduling optimization model is adjusted as needed, specifically depending on the overall operational objectives of the microgrid group.

[0073] In step S7, the microgrid operation model remains the same as that described in S3. The objective function can be set as needed, such as minimizing total operating costs.

[0074] Step S8, decomposing the aggregation mapping relationship, is as follows: First, perform an inverse affine transformation on the aggregated output power to obtain the power magnitude represented by the reference set. Then, through affine transformation, the adjustable power set of each individual is obtained, that is, the actual output power of each distributed resource unit is... .

[0075] Example This embodiment selects, as follows: Figure 4 The system shown is used for verification. In this case study, only a system diagram connecting a higher-level public network and a microgrid is used for illustration. The microgrid is connected to node 3 of the higher-level public network. The higher-level public network is configured with several conventional small generator sets or centralized energy storage and necessary network constraints; the microgrid is equipped with two micro gas turbines, two sets of energy storage devices, a temperature-controlled load cluster, an electric vehicle cluster, and a photovoltaic cluster. The above resources are managed separately by distributed aggregation units and provide aggregation capability descriptions to the microgrid management unit.

[0076] Decentralized aggregation unit-side aggregation (corresponding to steps S1–S2): Based on the operational constraints, power / energy boundaries, and adjustability of each distributed resource unit within its jurisdiction, the decentralized aggregation unit constructs a set of time-series feasible domains for each unit, and aggregates multiple resources of the same type or under the same management entity to form an equivalent flexible set and its de-aggregation mapping relationship. In this implementation case, the temperature-controlled load cluster, photovoltaic cluster, and electric vehicle cluster are aggregated respectively to obtain the adjustable power / energy range of each cluster within the scheduling cycle (e.g., the upper / lower limits at each moment). The aggregation result is illustrated as follows. Figure 5 As shown, from left to right, the equivalent adjustable power / energy boundaries of the temperature-controlled load cluster, the photovoltaic cluster, and the electric vehicle cluster are given respectively. These boundaries serve as the resource-side constraint inputs for the subsequent microgrid operation model.

[0077] Construction of the grid-connected power equivalent model (corresponding to steps S3–S5): After receiving the equivalent flexible sets of the above clusters, the microgrid management unit establishes a microgrid operation optimization model that includes distributed resource injection constraints, microgrid power flow constraints, voltage constraints, and line capacity constraints. Furthermore, it performs continuous-time processing or equivalent continuous processing on the power, voltage, and other time-varying variables in the model, enabling the model to characterize the variable changes within time intervals, forming a solvable continuous-time operation model of the microgrid. Based on this, the microgrid management unit aggregates the switching power capacity of the entire microgrid at the interconnection point into an active support power source model, obtaining a description of the adjustable power boundary and feasible region of the microgrid to the upper-level grid. To ensure that the equivalent model can truly reflect the internal feasibility of the microgrid, consistency checks are used to iteratively correct the boundary parameters, so that the final equivalent model can be decomposed into executable output commands for each resource within the microgrid. In this implementation case, the adjustable switching power range of the aggregated active support power source at each moment is illustrated as follows: Figure 6 As shown.

[0078] The system parameters, resource parameters, and predicted data of this implementation case were input into a pre-written computer program, and aggregation at the distributed aggregation unit side and aggregation of active support power sources at the microgrid side were performed respectively. The calculation time statistics for each stage are shown in Table 1. It can be seen that the calculation time is no more than five minutes for both the aggregation of distributed resources and the aggregation of active support power sources for the entire microgrid. For the specific application of the proposed method and the current case, this computational efficiency can meet the time requirements of day-ahead / intra-day rolling scheduling, indicating that the method of this application has good computational efficiency and engineering feasibility.

[0079] Table 1. Specific computation time for aggregation of each distributed resource cluster and microgrid-side active supporting power source.

[0080] Coordination layer scheduling and boundary power interaction plan issuance (corresponding to step S6): After obtaining the equivalent model of the active supporting power source that reflects the actual switching capacity of the microgrid, it is incorporated into the coordination layer optimal power flow model as an equivalent unit / power plant model. With the goal of minimizing the total system operating cost, and comprehensively considering the power output constraints of the coordination layer grid units, line power flow constraints, and active supporting power source operating capacity constraints, the tie-point switching power plan for each time period within the scheduling cycle is obtained. The active supporting power source switching power plan is illustrated as follows: Figure 7 As shown, it is then sent to the corresponding microgrid.

[0081] Microgrid Internal Decomposition and Cluster Scheduling (corresponding to step S7): The microgrid management unit uses the tie-point boundary power interaction plan issued by the upper-level power grid as boundary conditions and substitutes it into the microgrid continuous-time operation model to obtain the power scheduling instructions for each resource cluster (including micro gas turbines, energy storage devices, temperature-controlled load clusters, photovoltaic clusters, electric vehicle clusters, etc.) in each time period. The decomposed cluster power plans are illustrated below. Figures 8(a)-8(c) As shown, it is further distributed to each decentralized polymerization unit.

[0082] Decentralized aggregation unit-side de-aggregation and individual unit instruction generation (corresponding to step S8): After receiving the cluster scheduling instruction, each decentralized aggregation unit, based on the de-aggregation mapping relationship formed in steps S1–S2, de-aggregates the cluster-level scheduling instruction into output instructions for each distributed resource unit, and issues the output instructions for execution. The output power trajectory of each individual distributed resource is illustrated as follows. Figures 9(a)-9(c) As shown, the power / energy transformation trajectory of each distributed resource is not exactly the same as the cluster trajectory. This is due to the difference in the adjustability of each distributed resource itself.

[0083] In summary, the continuous-time hierarchical aggregation and scheduling framework for collaborative optimization operation of microgrid groups proposed in this application can be derived from... Figure 10 In summary, this forms a closed-loop scheduling process that includes "upper-level coordination layer - microgrid cluster - resource cluster - resource unit".

[0084] In summary, this application transmits only equivalent / aggregated data through each entity, without transmitting the precise internal data, thus preventing the exposure of detailed information within the microgrid group. Under this premise, by aggregating and transmitting capabilities and distributing them decomposably, and by introducing continuous-time / equivalent continuous modeling techniques, the ability to finely characterize variable changes and constraints within a time period is improved, thereby enhancing the feasibility and economic efficiency of collaborative operation.

[0085] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0086] Based on the methods in the above embodiments, this application provides an electronic device that may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor may invoke logical instructions stored in the memory to execute the methods in the above embodiments.

[0087] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, 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 application.

[0088] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0089] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0090] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0091] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0092] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0093] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0094] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A continuous-time hierarchical aggregation and collaborative scheduling method for active support of microgrids, characterized in that, include: In a microgrid cluster, each decentralized aggregation unit constructs a set of time-series feasible domains for each distributed resource unit based on the operating characteristics and constraints of each distributed resource unit within its jurisdiction. The distributed aggregation unit aggregates the time-series feasible domain set of all distributed resource units within its jurisdiction to obtain an equivalent flexible set with aggregation / deaggregation mapping relationship, which is then sent to the corresponding microgrid management unit. After receiving the equivalent flexible sets of each distributed resource cluster, the microgrid management unit constructs a microgrid operation model containing the constraints of the equivalent flexible sets, performs continuous-time processing on the time-series variables in the microgrid operation model, and obtains a solvable microgrid continuous-time operation model. The microgrid management unit aggregates the switching power capacity of the entire microgrid at the tie point into an active support power model based on the microgrid continuous-time operation model. The microgrid management unit performs consistency verification and iterative correction on the active support power source model and the microgrid continuous-time operation model, and sends the verified and corrected active support power source model to the microgrid group coordination layer. The microgrid group coordination layer incorporates the verified and corrected active support power source models of each microgrid into the coordination layer scheduling optimization model, solves for the switching power at each microgrid interconnection point, and sends it to the microgrid management unit. The microgrid management unit receives the switching power at the tie point, substitutes it as a boundary condition into the verified and corrected microgrid continuous-time operation model, solves for the power scheduling instructions corresponding to each distributed resource cluster, and sends them to the distributed aggregation unit. Based on the de-aggregation mapping relationship, the distributed aggregation unit de-aggregates the received power scheduling instructions into output instructions for each distributed resource unit and executes them.

2. The continuous-time layered aggregation and coordinated scheduling method of claim 1, wherein, The distributed aggregation unit obtains a maximum inner approximation set of Minkowski sums through affine transformation, thereby aggregating the temporally feasible domain set of all distributed resource units within its jurisdiction. (1) Define the reference adjustable power set ,in, This indicates that distributed resources are in a scheduling cycle. Reference power at each time point within the range, This represents the coefficient matrix extracted from each constraint, with dimension 1. , The number of constraints, the average value of the boundary parameters of the power set of each dispersed aggregation unit. , These are boundary parameters, determined by the upper and lower bounds of the constraints that different distributed resource units must satisfy. This indicates the number of distributed resource units managed by a single decentralized aggregation unit; (2) Define combination Represents the set of adjustable reference power. The equivalent adjustable capacity set of distributed resources managed by the approximately aggregated decentralized aggregation unit The affine transformation parameters are defined as a combination. Represents the set of adjustable reference power. The adjustable power set of each dispersed polymerization unit after approximate polymerization Affine transformation parameters: ； (3) Solve the following optimization model to achieve the maximum inner approximation of the true Minkowski sum; wherein denotes the trace of the matrix denotes the dual factor denotes the constraint​ (4) using the parameter combination obtained by solving , affine transformation is performed on the reference adjustable power set to obtain the equivalent adjustable capacity set of the distributed resources administered by the dispersion aggregation unit .

3. The continuous-time layered aggregation and coordinated scheduling method of claim 1, wherein, The microgrid operation model is as follows: The operating constraints of micro gas turbines are: ; wherein and are the active and reactive power output of the micro gas turbine, respectively, is the upper bound of is the power related factor of the micro gas turbine, the index denotes the bus to which the micro gas turbine is connected, the index indicates the corresponding time ; The charge and discharge constraints of energy storage devices include: in, and These represent the active and reactive power of the energy storage device, respectively. The reactive power is generated by the converter. and These represent the upper boundaries of the active power for discharging and charging the energy storage device, respectively. For the power-related factors of the energy storage device, The amount of energy stored in the energy storage device. These are the upper and lower bounds of the energy stored in the energy storage device. The energy loss rate of the energy storage device. For the charging efficiency of energy storage devices, The step size between adjacent time steps; The operation constraints of the distributed photovoltaic aggregation are: ; wherein, and Ppv,actand Ppv,reactiveare the active and reactive power of the distributed photovoltaic, respectively, Ppv,act,ubis the upper bound of the active power of the distributed photovoltaic, Ppv,act,ubis the upper bound of the active power of the distributed photovoltaic, The operation constraint after the temperature control load is aggregated is: ; wherein P is the power of the temperature-controlled load, and Pmaxand Pminare the upper and lower bounds of the power of the temperature-controlled load, respectively. The operational constraints of the aggregated electric vehicles include: wherein P is the power of the electric vehicle, and Pmaxand Pminare respectively the upper and lower bounds of the power of the electric vehicle, E is the amount of energy stored by the electric vehicle, Emaxand Eminare respectively the upper and lower bounds of the amount of energy stored by the electric vehicle, η is the charging efficiency of the electric vehicle; Microgrid DistFlow power flow operation models that ignore line losses include: in, and They represent Timetable The trend of meritorious and ineffective actions busbar The square of the voltage, and for upper and lower boundaries, and for upper and lower boundaries, and busbar voltage upper and lower boundaries, and Representing the lines respectively The reactance and resistance, For a set of routes, the subscript is... Indicates corresponding to the busbar Starting bus The route with the destination as the destination , These respectively represent connections to the busbar The active and reactive power of the load.

4. The continuous-time hierarchical aggregation and collaborative scheduling method as described in claim 1, characterized in that, The microgrid management unit, based on the microgrid's continuous-time operation model, aggregates the switching power capacity of the entire microgrid at the tie point into an active support power model, as detailed below: ； in, This indicates a connection to a higher-level coordination layer network node. Active support power supply, and for Corresponding to upper and lower bounds, This indicates the operational constraints of the active support power supply. For the identity matrix and The matrix formed This represents the maximum infeasibility power obtained from the solution. for and The resulting matrix, i.e., the power boundary, Indicates the scheduling period.

5. The continuous-time hierarchical aggregation and collaborative scheduling method as described in claim 1, characterized in that, The microgrid management unit performs consistency verification and iterative correction on the active support power source model, as detailed below: (a) Construct a two-layer optimization model for consistency verification that minimizes the difference between the boundary of the active support power exchange power and the actual contact point exchange power boundary. (b) Find the dual of the bottom layer of the bi-level optimization model, and use the kkt condition to correct the product term of the variables to obtain the single-level maximization dual problem. Set the number of iterations s=1. (c) Solve the single-layer dual problem to obtain the maximum infeasible boundary exchange power. Combine the original operating constraints of the microgrid to construct a problem to minimize the difference between the boundary exchange power and the feasible boundary. Solve the problem to obtain the feasible power closest to the maximum infeasible boundary exchange power. (d) Using the operating boundary parameters of the most recent feasible power-constrained active support power model, construct a maximization problem about the power boundary of the active support power model; (e) Solve the maximization problem to obtain a new set of power boundaries for the active support power source model. Substitute these into the single-layer maximization dual problem and return to step (c), and set s=s+1. (f) Repeat steps (c)-(e) until the optimal value of the single-layer maximization dual problem is 0, and obtain the active support power source model that reflects the actual power exchange capability of the microgrid at the junction point.

6. The continuous-time hierarchical aggregation and cooperative scheduling method as described in claim 5, characterized in that, The specific two-layer optimization model for consistency verification is as follows: ； ； in, These represent the positive and negative differences between the adjustable power of the active support power supply and the actual support power at the microgrid interconnection point, respectively. and Let a and b represent the coefficient matrices extracted from the inequalities and equality constraints in the original microgrid operation model, respectively, where a and b are the remaining constants in the constraints. The coefficient matrix is ​​extracted from the constraints related to boundary exchange power. This represents all variables in the microgrid operation model. This represents the equality constraints related to the power transmitted at the tie points in a microgrid. For the identity matrix and The matrix formed Indicates the scheduling period. This represents the maximum infeasibility power obtained from the solution. Indicates the first The iteration yielded , This is a matrix formed by the upper and lower bounds of the active support power at time t, which is connected to the network node k in the upper coordination layer.

7. The continuous-time hierarchical aggregation and cooperative scheduling method as described in claim 5, characterized in that, The specific single-layer maximization dual problem is as follows: ； ； in, , , , As dual variables, To represent the transpose of a matrix, This is a matrix formed by the upper and lower bounds of the active support power supply at time t, connected to the network node k in the upper coordination layer. and For the remaining constants in the constraints, and These represent the coefficient matrices extracted from the inequalities and equality constraints in the original microgrid operation model, respectively. The coefficient matrix is ​​extracted from the constraints related to boundary exchange power. For the identity matrix and The matrix formed Indicates the scheduling period. This represents the actual aggregated set of distributed resource clusters managed by the decentralized aggregation unit. These are slack variables.

8. The continuous-time hierarchical aggregation and cooperative scheduling method as described in claim 5, characterized in that, The specific problem concerning minimizing the boundary exchange power and the difference between feasible boundaries is as follows: ； ； in, This represents the maximum infeasibility power obtained from the solution. To obtain the maximum infeasible power The most recent feasible power, The coefficient matrix is ​​extracted from the constraints related to boundary exchange power. This represents all variables in the microgrid operation model. This represents the equality constraints related to the power transmitted at the tie points in a microgrid. This represents the domain of operational variables in the microgrid operation model.

9. The continuous-time hierarchical aggregation and cooperative scheduling method as described in claim 5, characterized in that, The specific problem of maximizing the power boundary of the active support power source model is as follows: ； ； in, Represents the identity matrix. To represent the transpose of a matrix, Indicates the first The iteration yielded , This is a matrix formed by the upper and lower bounds of the active support power supply at time t, connected to the network node k in the upper coordination layer. As slack variables, For the identity matrix and The matrix formed Indicates the scheduling period. To find the nearest feasible power to the maximum infeasible power, This represents the matrix dot product operator.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the continuous-time hierarchical aggregation and cooperative scheduling method as described in any one of claims 1 to 9.

Images