A dual-port equivalent cluster aggregation method

Abstract

This application belongs to the field of power grid dispatching technology, specifically disclosing a two-port equivalent cluster aggregation method. This application establishes independent aggregation operating intervals for the two ports by solving a specific robust optimization problem, forming initial aggressive boundaries and building an initial high-dimensional polyhedral model. The key improvement lies in subsequently solving the conditional extremum of the power of the other port under the constraints of the operating interval of one port, thereby transforming the physical coupling relationship into a series of precise boundary points and reducing computational complexity. Finally, these boundaries are used to cut the initial, aggressive high-dimensional model, eliminating infeasible spaces caused by neglecting coupling, and quickly generating feasible two-port aggregation intervals. This systematically solves the problems of large model errors and low aggregation efficiency caused by two-port interconnection.

Description

Technical Field

[0001] This application belongs to the field of power grid dispatching technology, specifically the field of intelligent dispatching system technology, and more specifically, to a dual-port equivalent cluster aggregation method. Background Technology

[0002] Currently, low-voltage distribution networks exhibit three new operational characteristics: First, bidirectional power flow, as the high daytime output of renewable energy sources like photovoltaics results in a net load of less than zero on the low-voltage distribution network, leading to reverse power output; second, significant fluctuations and strong intermittency, due to the increasing penetration rate of renewable energy sources and the highly flexible generation and consumption characteristics of distributed energy sources such as photovoltaics and electric vehicles; and finally, management difficulties, as the total power of distributed energy sources is large, while individual power is small, and they are distributed throughout the distribution network, placing significant communication and computational burdens on the distribution network management side.

[0003] If distributed energy resources connected to the grid structure within a certain range are regarded as an equivalent cluster, and then aggregated to form an equivalent cluster that interacts with the outside world, the number of constraints and variables that the upper-level power grid needs to consider during dispatching can be reduced, thereby greatly reducing the complexity of power system operation and improving the efficiency of distributed energy management.

[0004] To facilitate the interconnection of power resources, equivalent clusters often form two ports connected to the external power grid. The aggregation of equivalent clusters requires consideration of both ports simultaneously. However, the interconnection characteristics between the two ports often greatly increase the difficulty of aggregation. How to efficiently perform aggregation and express the aggregation result in a simple form is also a current technological challenge.

[0005] In addition, considering the operating characteristics and network constraints of different types of distributed energy, it is also necessary to ensure that all scheduling instructions within the aggregation interval can be deaggregated in the equivalent cluster. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this application is to provide a dual-port equivalent cluster aggregation method, which aims to solve the problem of low aggregation efficiency caused by the limited interconnection characteristics between dual ports in existing dual-port equivalent cluster aggregation methods.

[0007] The first aspect of this application relates to a method for aggregating a dual-port equivalent cluster, comprising: modeling the dual-port equivalent cluster based on the distributed energy resources set in the equivalent cluster and obtaining model constraints; solving the aggregation problem based on the model constraints to obtain a first aggregation operating interval for the first port and a second aggregation operating interval for the second port; the aggregation problem is a robust optimization problem aimed at finding the aggregation operating interval of the dual-port equivalent cluster under all scheduling scenarios; aggregating an initial high-dimensional polyhedron model based on the first and second aggregation operating intervals and generating initial high-dimensional constraints; obtaining the power boundary values ​​of each port under the operating interval constraints of the other port based on the model constraints and the initial high-dimensional constraints; and cutting the initial high-dimensional polyhedron model based on multiple power boundary values ​​to obtain a target high-dimensional polyhedron model for aggregating the dual-port equivalent cluster.

[0008] In one embodiment, the model constraints include: a first model constraint for the first port and a second model constraint for the second port; modeling the dual-port equivalent cluster based on the distributed energy resources set in the equivalent cluster and obtaining the model constraints, solving the aggregation problem based on the model constraints, and obtaining the first aggregation operating range for the first port and the second aggregation operating range for the second port respectively, including: obtaining the first model constraints based on the distributed energy resources set in the equivalent cluster and the node positions of the first port, then solving the aggregation problem based on the first model constraints to obtain the first aggregation operating range for the first port; obtaining the second model constraints based on the distributed energy resources set in the equivalent cluster and the node positions of the second port, then solving the aggregation problem based on the second model constraints to obtain the second aggregation operating range for the second port.

[0009] In one embodiment, solving the aggregation problem based on the first model constraints includes: constructing the aggregation problem as a min-max robust optimization problem with an objective function of 0, and performing a dual transformation on the inner max problem to construct a min dual problem in multiple iterations; solving the dual problem to obtain the worst scheduling instruction and the corresponding effective constraint indicator variable under the current iteration, and obtaining the nearest feasible scheduling instruction based on the worst scheduling instruction; iteratively updating the boundary constraints of the first aggregation running interval based on the effective constraint indicator variable and the feasible scheduling instruction, and performing the steps of solving the dual problem until the solution of the dual problem is 0, so as to confirm that the current boundary constraints are the final result of the first aggregation running interval.

[0010] In one embodiment, the initial high-dimensional constraint is:

[0011] ; ; ;

[0012] Where Y is and The vector formed; This represents the exchange power between the first port and the power grid. This refers to the power exchanged between the second port and the power grid. yes and- The matrix formed; Total number of time periods for OK The identity matrix of columns; yes , , and The vector formed; This is the upper limit of the exchange power between the first port and the power grid; This is the lower limit of the exchange power between the first port and the power grid; This is the upper limit of the exchange power between the second port and the power grid; This is the lower limit of the exchange power between the second port and the power grid.

[0013] In one embodiment, based on model constraints and initial high-dimensional constraints, the power boundary values ​​of each port under the constraints of the operating interval of another port are obtained, including: based on model constraints and initial high-dimensional constraints, obtaining the power boundary values ​​of the first port under the upper limit condition of the exchange power between the second port and the grid and the lower limit condition of the exchange power between the second port and the grid in each time period; based on model constraints and initial high-dimensional constraints, obtaining the power boundary values ​​of the second port under the upper limit condition of the exchange power between the first port and the grid and the lower limit condition of the exchange power between the second port and the grid in each time period.

[0014] In one embodiment, the model constraints are:

[0015] ; ; ;

[0016] in, Y is a vector consisting of all decision variables; and The vector formed; This represents the exchange power between the first port and the power grid. This refers to the power exchanged between the second port and the power grid. , and For parameter matrices; , The coefficient vector is used; both the parameter matrix and the coefficient vector are determined by the operational constraints, output constraints, and network constraints of the equivalent cluster; the power boundary values ​​include the maximum and minimum exchange power; the maximum exchange power of the first port under the upper limit condition of the exchange power between the second port and the grid is:

[0017] ;

[0018] ; ;

[0019] in, For initial high-dimensional constraints; yes and- The matrix formed; Total number of time periods for OK The identity matrix of columns; yes , , and The vector formed; This is the upper limit of the exchange power between the first port and the power grid; This is the lower limit of the exchange power between the first port and the power grid; This is the upper limit of the exchange power between the second port and the power grid; This is the lower limit of the exchange power between the second port and the power grid; The relationship matrix for port switching power.

[0020] In one embodiment, an initial high-dimensional polyhedron model is cut based on multiple power boundary values ​​to obtain a target high-dimensional polyhedron model for aggregating a two-port equivalent cluster. This includes: generating multiple two-dimensional polygonal models based on multiple power boundary values ​​in each time period; cutting the initial high-dimensional polyhedron model based on the multiple two-dimensional polygonal models to generate cutting results; converting the cutting results into a target high-dimensional polyhedron model and outputting target high-dimensional constraints to aggregate a two-port equivalent cluster.

[0021] In a second aspect, this application provides an electronic device including a memory and one or more processors; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code including computer instructions; one or more processors invoke the computer instructions to cause the electronic device to perform the method of the first aspect.

[0022] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on an electronic device, cause the electronic device to perform the method of the first aspect.

[0023] Fourthly, this application provides a computer program product, including a computer program or instructions, which, when executed on an electronic device, cause the electronic device to perform the method of the first aspect.

[0024] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art:

[0025] The dual-port equivalent cluster aggregation method provided in this application effectively overcomes the limitations of traditional methods by introducing a progressive optimization process from independent intervals to coupling boundaries and then to model cutting.

[0026] First, by solving a robust min-max optimization problem with an objective function of 0, independent aggregation operating ranges were established for the two ports, and an initial high-dimensional polyhedron model was built, providing a benchmark for subsequent analysis. However, this is only a preliminary and radical boundary. Due to the lack of consideration for interconnection characteristics, there are situations where the two ports cannot be realized simultaneously within their respective aggregation operating ranges.

[0027] The key improvement lies in solving the power condition extrema of this port under the constraints of the other port's operating range. This step rigorously introduces physical coupling and operational constraints between the two ports, thereby correcting the independent and radical boundaries of each port into a series of power boundary points reflecting the interconnection relationship. This step significantly reduces complexity. The final step uses these points to cut the initial radical high-dimensional polyhedron model, eliminating infeasible operating spaces that exist due to neglecting coupling, ultimately quickly generating a relatively accurate and feasible target high-dimensional polyhedron model.

[0028] Compared with existing technologies, this application does not stop at the simple superposition of independent boundaries, but systematically solves the core problems of difficulty in solving aggregation models and low aggregation efficiency caused by the dual-port interconnection characteristics through a coherent technical chain of constraint introduction, boundary correction and model cutting, thereby achieving faster and more efficient cluster equivalent aggregation. Attached Figure Description

[0029] Figure 1 This is a conceptual diagram of the equivalent power operation of the power grid interaction between the power cluster and the upper-level power grid, provided in the embodiments of this application.

[0030] Figure 2 This is a flowchart illustrating the dual-port equivalent cluster aggregation method provided in the embodiments of this application;

[0031] Figure 3 This is a topology diagram of an equivalent cluster based on a 21-node system provided in an embodiment of this application;

[0032] Figure 4 This is a schematic diagram of the process for solving the aggregation problem provided in an embodiment of this application;

[0033] Figure 5 This is a daily output power distribution diagram of distributed photovoltaic systems provided in the embodiments of this application;

[0034] Figure 6 This is a load quantization diagram of each node in the intraday equivalent cluster provided in the embodiments of this application;

[0035] Figure 7 These are the aggregation operation ranges of the first port and the second port obtained by aggregation according to the embodiments of this application;

[0036] Figure 8 This is a schematic diagram of the rectangle corresponding to the high-dimensional polyhedron model provided in this application embodiment for each hour;

[0037] Figure 9 These are the maximum and minimum value points on each side of the rectangle obtained in each time period, as provided in the embodiments of this application.

[0038] Figure 10 This is an aggregated operation interval diagram of each time period within a 24-hour day, considering the interconnection characteristics of two ports, provided by the embodiments of this application;

[0039] Figure 11 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] To facilitate power grid interconnection, equivalent clusters often form two ports connected to the external power grid, and the aggregation of equivalent clusters requires consideration of both ports simultaneously. For example... Figure 1 As shown, Figure 1 This is a conceptual diagram of the equivalent power operation of the cluster and the upper-level power grid provided in the embodiments of this application.

[0046] Based on this architecture, the dual-port design of the equivalent cluster not only enhances its interaction with the upstream power grid but also provides a foundation for the cluster's flexible operation and market participation. Through the first and second ports, namely port 1 and port 2 in the diagram, the equivalent cluster can simultaneously receive dispatch instructions or market signals and accept network constraints from the external power grid. Within this framework, the equivalent cluster operator can integrate internal distributed photovoltaic, micro gas turbine, energy storage, and load resources to form feasible solutions that meet the dual-port operating range, ensuring internal operational constraints while responding to upstream power grid requirements. This model improves the grid's ability to coordinate distributed resources and supports the cluster's dispatchability and economic viability in the electricity market.

[0047] However, the interconnectivity between dual ports often significantly increases the difficulty of aggregation. How to efficiently perform aggregation and express the aggregation result in a concise form remains a challenge for current technology. Furthermore, considering the operational characteristics and network constraints of different types of distributed energy sources, it is also necessary to ensure that all scheduling instructions within the aggregation interval can be de-aggregated in the equivalent cluster.

[0048] Based on this, this application proposes an embodiment of a dual-port equivalent cluster aggregation method. Please refer to... Figure 2 , Figure 2 This is a flowchart illustrating the dual-port equivalent cluster aggregation method provided in this application embodiment.

[0049] In this embodiment, the dual-port equivalent cluster aggregation method includes steps S10 to S40.

[0050] Step S10: Model the dual-port equivalent cluster based on the distributed energy set in the equivalent cluster and obtain the model constraints. Then solve the aggregation problem based on the model constraints and obtain the first aggregation running interval of the first port and the second aggregation running interval of the second port respectively.

[0051] It should be noted that modeling a two-port equivalent cluster based on distributed energy resources within the equivalent cluster refers to constructing a mathematical model that characterizes the operational characteristics of each distributed energy resource within the equivalent cluster and its coupling relationship with the two-port power. Distributed energy resources include, but are not limited to, micro gas turbines, photovoltaic power generation units, energy storage systems, and various loads. Model constraints cover the physical operational constraints of these devices, such as the ramp rate of the micro gas turbine, the charging and discharging power and capacity limits of the energy storage, and the output prediction range of the photovoltaic system, as well as network constraints of the port connection points determined by the external power grid, such as voltage safety limits.

[0052] Please refer to Figure 3 , Figure 3 This is a topology diagram of an equivalent cluster based on a 21-node system provided in an embodiment of this application. Figure 3 Taking the distributed energy resources existing in China as an example, we model a dual-port equivalent cluster.

[0053] exist Figure 3 In this equivalent cluster, the components are micro gas turbines, energy storage units, and distributed photovoltaics. The following needs to be defined: This represents the active power / reactive power output of the micro gas turbine. This represents the active power / reactive power output of the energy storage unit. This represents the stored energy value. This represents the active power / reactive power output of distributed photovoltaic systems. The active power / reactive power flowing on the transmission line between nodes. The square of the node voltage; For time period, For nodes.

[0054] Based on the above definitions, the following constraints are established to represent the restrictive relationships defined above:

[0055] (1); (2);

[0056] (3);

[0057] It should be noted that equations (1) to (3) are the operating constraints of the micro gas turbine.

[0058] (4); (5);

[0059] (6); (7);

[0060] It should be noted that equations (4) to (7) are the operating constraints of the energy storage unit.

[0061] (8);

[0062] (9);

[0063] It should be noted that equations (8) and (9) are the output constraints of distributed photovoltaic systems.

[0064] (10); (11);

[0065] (12); (13);

[0066] (14); (15);

[0067] It should be noted that equations (10) to (15) are network constraints.

[0068] Specifically, These represent the upper and lower limits of the power output of the micro gas turbine. The upper and lower limits of power ramp-up for micro gas turbines; These are the upper limits for the power generation and energy storage capacity of the energy storage unit. These are the upper and lower limits for energy storage. The self-discharge rate of the energy storage unit; This refers to the power generation of distributed photovoltaic systems. This represents the upper limit of the curtailment rate for distributed photovoltaic power. Active / reactive load, These are the upper and lower limits of the active power of the transmission line. These are the upper and lower limits of the reactive power of the transmission line. These are the upper and lower limits of the square of the node voltage. For the resistance and inductance of the transmission line; These represent the phase angles of a micro gas turbine, an energy storage unit, and a distributed photovoltaic system, respectively. The superscript "max" indicates the upper limit, and the superscript "min" indicates the lower limit.

[0069] It should be noted that the model constraints of the above equivalent cluster can be reorganized into the following compact form:

[0070] (16);

[0071] (17);

[0072] (18);

[0073] Understandably, X is a vector composed of all decision variables, and Y is the magnitude of the total power exchanged between the first and second ports and the upstream power grid. and The vectors formed; Equation (16) corresponds to all inequality constraints, and Equation (17) corresponds to all equality constraints; parameter matrix sum coefficient vector All of these are determined by the operational constraints, output constraints, and network constraints in the equivalent cluster mentioned above; These represent the power exchanged between the first and second ports and the upstream power grid, respectively.

[0074] It should be noted that the aggregated operating range constraint of the two ports can be summarized in the following form:

[0075] (19); (20);

[0076] (twenty one); (twenty two);

[0077] It is understandable that equations (19) to (22) represent the constraints corresponding to the equivalent cluster aggregation operation range. for The relationship matrix between the power exchanged at each port.

[0078] Understandable and Corresponding to and , and The upper and lower bounds for the output power of equivalent cluster port 1 and port 2 are as follows:

[0079] (twenty three); (twenty four); (25); (26);

[0080] in, for OK The identity matrix of columns.

[0081] It should be noted that the aggregation problem is a robust optimization problem aimed at finding the aggregated operating range of a two-port equivalent cluster under all scheduling scenarios. The aggregated operating range refers to the set of all possible operating points of the two ports of the equivalent cluster, provided that all internal operating constraints are satisfied.

[0082] Understandably, for the equivalent cluster, the scheduling instructions from the external power grid are uncertain. However, these instructions are based on the aggregated operating range submitted by the dual-port equivalent cluster. Therefore, we aim to find an aggregated operating range that ensures the equivalent cluster can successfully execute any scheduling instructions issued by the external power grid based on this range.

[0083] Therefore, it can be understood that the robust optimization problem proposed in this invention is a decision optimization problem. Its objective is not to maximize or minimize a certain performance index while considering uncertainties within the equivalent cluster, but rather to determine the feasibility of the aggregated operating range of the equivalent cluster. This differs mathematically from traditional economic scheduling or optimal power flow problems in that its objective function is constructed as a constant, such as 0. The solver's task is to search the boundary of the feasible solution space that makes the problem solvable, rather than finding a single optimal solution.

[0084] Specifically, the aggregation problem can be solved using a min-max robust optimization problem with an objective function of 0. The min-max robust optimization problem is an optimization method that aims to find a feasible decision scheme under the worst uncertainty scenario. In this context, solving it with an objective function of 0 aims to focus on finding a feasible power boundary that satisfies all model constraints, rather than pursuing a specific economic objective.

[0085] It should be noted that the aggregation problem can be expressed in the following form:

[0086] (27).

[0087] It is understandable that RES refers to the aggregation problem, and P is the switching power of the port. Based on the above model constraints and aggregation runtime constraints, equation (27) can be iteratively obtained. Before iterating to the aggregation runtime, the solution to the problem is... When the iteration reaches the aggregate running interval, the solution to the problem is 0. Therefore, we hope to find an aggregate running interval such that the solution to problem (27) is... .

[0088] Specifically, although the overall model expression is consistent, the differences in the physical locations (i.e., nodes) of the first and second ports, for example... Figure 3 Located at nodes 1 and 21 respectively, the topology, line parameters, and electrical distances of the power grids are different, which means that the key parameters in the model constraints will actually change when solving for the maximum and minimum operating capacity of each port individually.

[0089] Specifically, when solving for the first port (corresponding to node 1), the network constraint parameters in the model constraints (such as the voltage reference of node 1, the impedance and capacity limits of the lines connected to it) are different from the corresponding parameters when solving for the second port (corresponding to node 21), because these parameters are essentially node-dependent. Therefore, during the modeling and optimization process, it is necessary to explicitly divide the model constraints into first model constraints for the first port and second model constraints for the second port to ensure that the aggregate operating range calculation for each port can accurately reflect the specific electrical environment of its node.

[0090] From an implementation perspective, this can be accomplished through a constraint management module configured in the energy management system: the hardware of this module is server-based, and the software integrates a power grid model database. It can automatically call the corresponding parameter set according to the node identifiers of the port connections, such as node 1 and node 21, and generate and load the first model constraint and the second model constraint into the optimization solver respectively.

[0091] In alternative solutions, if explicit partitioning is not required, a parameterized unified constraint form can be adopted. This involves introducing node index variables into the constraint equations, allowing the corresponding node parameters to be dynamically substituted when solving different ports using the same set of equations. This method of partitioning model constraints by port fundamentally ensures that the constraints relied upon in solving the min-max robust optimization aggregation problem in step S10 are consistent with the actual physical connections of each port. This makes the obtained first and second aggregation operating intervals not only mathematically rigorous and feasible but also accurately characterize the independent operating boundaries of each port in the real power grid, providing a correct constraint basis for the subsequent de-aggregation of scheduling commands.

[0092] Therefore, the above solution process can be decomposed into: obtaining the first model constraint based on the distributed energy and node positions of the first port in the equivalent cluster, and then solving the aggregation problem based on the first model constraint to obtain the first aggregation operation range of the first port; obtaining the second model constraint based on the distributed energy and node positions of the second port in the equivalent cluster, and then solving the aggregation problem based on the second model constraint to obtain the second aggregation operation range of the second port.

[0093] It should be noted that the first aggregation operating range of the first port and the second aggregation operating range of the second port are the output results of this step. This aggregation operating range is a simplified representation of the aggregation result, usually expressed as a set of feasible regions of active power, such as the upper and lower bounds of active power over a period of time. This range clearly defines the range of power that each port can safely exchange with the external power grid under given model constraints. The devices or systems implementing this step include an energy management system configured at an equivalent cluster operator. Its hardware can be an industrial server consisting of a central processing unit, memory, and communication interfaces, while the software runs an algorithm program containing an optimization solver (such as CPLEX or Gurobi).

[0094] It should be noted that this step fundamentally ensures that the obtained aggregated operating range strictly meets all internal and external physical and safety constraints, and its range format facilitates reporting to the superior dispatch center or the electricity market, significantly reducing communication and decision-making complexity. Furthermore, since this range is aggregated based on precise model constraints of all distributed energy resources within the cluster, it mathematically ensures that any dispatch instruction issued by the superior grid that falls within this aggregated operating range, such as a specific port power setpoint, can be decomposed into feasible dispatch instructions for each micro gas turbine, energy storage, and photovoltaic unit through the corresponding de-aggregation algorithm. This satisfies the key requirement that dispatch instructions can be de-aggregated within an equivalent cluster, achieving consistency between the aggregation and de-aggregation processes.

[0095] In one specific implementation, solving the aggregation problem can be done as follows: Figure 4 The process is shown below. Figure 4 This is a schematic diagram of the process for solving the aggregation problem provided in the embodiments of this application.

[0096] exist Figure 4 In the process, solving the aggregation problem includes steps S11 to S13.

[0097] Step S11: The aggregation problem is constructed as a min-max robust optimization problem with an objective function of 0, and the inner max problem is transformed into a dual min dual problem in multiple iterations.

[0098] It should be noted that since the two ports are calculated independently, the first port will still be used as an example here.

[0099] Specifically, the aggregation problem of the first port can be expressed in the following form:

[0100] (28);

[0101] (29);

[0102] The constraints used to solve for the first port are equations (16), (17), (19), and (20). The aggregation of the first port of the equivalent cluster is achieved by iteratively solving for a constraint that allows equation (28) to be solved to zero. ;when Before iterating to the aggregation runtime, the solution to the problem is: ,when When iterating to the aggregation run interval, the solution to the problem is 0; the solution process for the aggregation run interval of the second port can be obtained in the same way.

[0103] Performing a dual transformation on the inner max problem, the min dual problem in these multiple iterations is actually the th... A unified min dual problem in the next iteration is shown below:

[0104] (30);

[0105] (31);

[0106] (32);

[0107] Here, DES refers to the unified min dual problem. As dual variables, Lagrange multipliers under KKT conditions Let it be a sufficiently large positive number in the "Big M method". For Boolean variables, For parameter matrices, Total number of time periods for OK The identity matrix of columns.

[0108] Step S12: Solve the dual problem to obtain the worst scheduling instruction and the corresponding effective constraint indicator variable under the current iteration, and obtain the nearest feasible scheduling instruction based on the worst scheduling instruction.

[0109] It should be noted that initial values ​​need to be entered before the formal iteration, as shown in the following formula:

[0110] (33); (34);

[0111] (35); (36);

[0112] in, for The Each element.

[0113] It is understandable that solving problem (30) can yield the current... Worst-case scheduling instruction under the given conditions and Boolean variables indicating valid constraints Boolean variables The value of (0 or 1) directly defines and indicates whether the corresponding inequality constraint is a valid constraint. The complementary relaxation condition in the KKT conditions (represented as a constraint in the problem) (And the sign restrictions on λ and relaxation) through the "Big M" constraint and The values ​​of form a mathematical linkage, ensuring that =0 (effective constraint) and λ>0 occur simultaneously, while =1 (invalid constraint) and λ=0 occur simultaneously. Therefore, within this modeling framework, binary variables... These are decision variables specifically designed to identify effective constraints; the two are equivalent.

[0114] Step S13: Based on the effective constraint indicator variable and feasible scheduling instruction, iteratively update the boundary constraints of the first aggregated running interval, and execute the step of solving the dual problem until the solution of the dual problem is 0, so as to confirm that the current boundary constraints are the final result of the first aggregated running interval.

[0115] It should be noted that the worst-case scheduling instruction is used. Find the nearest feasible scheduling instruction as follows:

[0116] (37);

[0117] (38).

[0118] It should be noted that, according to the instructions, effective constraints apply. and feasible scheduling instructions iteration The specific iterations are carried out using the following optimization problems (39) to (40):

[0119] (39);

[0120] (40).

[0121] Specifically, It is a Boolean variable; For and Vectors of the same dimension; let and Proceed to step S12 until the unified min dual problem result is found. .

[0122] Understandably, the process of solving for the second port is the same as that of solving for the first port. Ultimately, this will yield the first aggregated operating range of the first port and the second aggregated operating range of the second port. These two aggregated operating ranges represent the upper and lower bounds of the switching capabilities achievable by the first port and the second port, respectively, across multiple time periods throughout the day, without considering interconnection factors. In actual interconnection processes, these two ranges are necessarily mutually restrictive; therefore, their individual switching capabilities as a whole should be less than these limits.

[0123] Step S20: Aggregate the initial high-dimensional polyhedron model based on the first aggregation running interval and the second aggregation running interval, and generate the initial high-dimensional constraints.

[0124] It should be noted that aggregation refers to a synthesis operation, the input of which is the first aggregation operating interval and the second aggregation operating interval obtained in step S10, respectively defined on the power spaces of the first and second ports. These operating intervals can be mathematically represented as convex polyhedra. The initial high-dimensional polyhedral model is a higher-dimensional geometric model constructed by synthesizing the polyhedral operating intervals of the two ports mentioned above. This model fully describes the set of solutions for the external interaction power of the equivalent cluster in multiple time periods without considering interconnection factors.

[0125] It should be noted that the first and second aggregation operation intervals are integrated into the following H-representation of the high-dimensional polyhedron, which is the initial high-dimensional polyhedron model:

[0126] (41); (42);

[0127] (43); (44).

[0128] Equations (41) to (44) are the initial high-dimensional constraints. This represents the exchange power between the first port and the power grid. This refers to the power exchanged between the second port and the power grid. yes and- The matrix formed; Total number of time periods for OK The identity matrix of columns; yes , , and The vector formed; This is the upper limit of the exchange power between the first port and the power grid; This is the lower limit of the exchange power between the first port and the power grid; This is the upper limit of the exchange power between the second port and the power grid; This is the lower limit of the exchange power between the second port and the power grid.

[0129] Understandably, this high-dimensional polyhedron A rectangle corresponds to each time period; this rectangle has the power of the first port as the horizontal axis and the power of the second port as the vertical axis, with the four sides of the rectangle corresponding to the power of the second port within that time period. and Quantity.

[0130] Understandably, because the interconnection characteristics of two ports were not considered when solving for the two independent aggregation operating regions, this initial high-dimensional polyhedron model is actually a radical aggregation. Due to actual interconnection constraints, many regions in this model are unrealizable.

[0131] Step S30: Based on model constraints and initial high-dimensional constraints, obtain the power boundary values ​​of each port under the constraints of the other port's operating interval.

[0132] Understandably, model constraints ensure that any obtained scheduling instruction is physically feasible, meaning it can be executed by devices within the cluster; while initial high-dimensional constraints ensure that the scheduling instruction lies within the aggregate feasible region of any single-port operation. Model constraints and initial high-dimensional constraints effectively define the solution scope, significantly reducing the computational load.

[0133] Understandably, the power boundary values ​​of each port under the constraint of the other port's operating range are obtained separately. For the first port, the reachable boundary of the power of the first port is calculated under the premise that the power of the second port is constrained within its second aggregate operating range; and vice versa. This is how the precise operating boundary under the mutual coupling and constraint of the two ports is obtained. Here, the constraint selected in this application is the boundary value of the aggregate operating range.

[0134] Understandably, based on the above, step S30 is divided into obtaining the power boundary value of the first port under the upper limit condition of the exchange power between the second port and the power grid and the lower limit condition of the exchange power between the second port and the power grid in each time period, based on model constraints and initial high-dimensional constraints; and obtaining the power boundary value of the second port under the upper limit condition of the exchange power between the first port and the power grid and the lower limit condition of the exchange power between the second port and the power grid in each time period, based on model constraints and initial high-dimensional constraints.

[0135] It should be noted that the power boundary values ​​include the maximum and minimum exchange power. Taking the first port as an example again, we solve for the values ​​satisfying the following conditions on each side of the rectangle: , The maximum and minimum points are expressed as follows:

[0136] (45); (46);

[0137] It should be noted that equations (45) and (46) represent the upper limit of the exchange power with the power grid at the second port. Under constraints, seek the maximum power at the first port on the right side of the rectangle.

[0138] (47); (48);

[0139] It should be noted that equations (47) and (48) represent the lower limit of the exchange power with the power grid at the second port. Under the constraints, we seek the maximum power at the first port on the left side of the rectangle.

[0140] (49); (50);

[0141] It should be noted that equations (49) and (50) represent the upper limit of the exchange power with the power grid at the second port. Under the constraints, we seek the minimum power at the first port on the right side of the rectangle.

[0142] (51); (52);

[0143] It should be noted that equations (51) and (52) represent the lower limit of the exchange power with the power grid at the second port. Under constraints, we seek the minimum power at the first port on the left side of the rectangle. Similarly, we obtain the maximum and minimum values ​​at the second ports on the top and bottom sides of the rectangle for each time period under the power exchange constraint at the first port. The study found that the maximum and minimum values ​​on the same side are generally at the same point.

[0144] It is understandable that these eight values ​​are the power boundary values, which are the power boundary values ​​of each port under the constraints of the operating range of the other port, representing the power extrema of the joint feasible region boundary of the two ports. The operating range defined by these extrema strictly satisfies all physical coupling and operating constraints between the ports. Therefore, any power combination point within this range is a feasible operating point for the system to be safely scheduled. Step S40 below provides one way to define the operating range.

[0145] Step S40: Cut the initial high-dimensional polyhedron model based on multiple power boundary values ​​to obtain the target high-dimensional polyhedron model for aggregating the dual-port equivalent cluster.

[0146] It should be noted that "cutting" is an optimization term, specifically referring to adding new linear inequality constraints to an existing initial high-dimensional polyhedral model to gradually reduce redundant feasible regions in the model that do not satisfy real physical constraints. The target high-dimensional polyhedral model refers to the convex polyhedral model obtained after a series of cuts, which accurately describes the externally schedulable range of the two-port equivalent cluster, with unchanged dimensions but a more accurate volume.

[0147] In one feasible implementation, step S40 includes: generating multiple two-dimensional polygonal models based on multiple power boundary values ​​in each time period; cutting the initial high-dimensional polyhedron model based on the multiple two-dimensional polygonal models to generate cutting results; converting the cutting results into a target high-dimensional polyhedron model and outputting the target high-dimensional constraints to aggregate dual-port equivalent clusters.

[0148] It is understandable that the maximum and minimum points on each edge obtained in each time period can be rewritten as follows: V- represents a two-dimensional polygon. :

[0149] (53).

[0150] It should be noted that, It is a non-negative constant. For time period The set of points containing the maximum and minimum values ​​on each edge obtained in the above process. For point set The first in One point, For point set The total number of points in the array.

[0151] It is understood that the method used in this embodiment is to directly solve for the feasible region considering interconnection characteristics and then cut it. In addition, the indirect method of first solving for the infeasible region considering interconnection characteristics and then cutting it to obtain the feasible region is still a cutting method in this embodiment, and will not be described in detail here.

[0152] It is understandable that this two-dimensional polygon is actually the range formed by connecting multiple maximum and minimum points sequentially within the same time period. Due to the initial high-dimensional constraints, the result of cutting the initial high-dimensional polyhedron model is the region enclosed by itself.

[0153] It should be noted that all time periods V- represents a two-dimensional polygon. Transform into H-representation of high-dimensional polyhedra as follows:

[0154] (54).

[0155] It should be noted that, The power values ​​of the first and second ports during the time period. and V- represents a two-dimensional polygon. The transformed parameter matrix and coefficient vector.

[0156] Specifically, the H-representation polyhedra for all time periods are integrated into a high-dimensional polyhedron representing all 24 time periods as follows:

[0157] (55).

[0158] Therefore, equation (55) is the desired dual-port aggregation operation range, and the scheduling instructions for power exchange in this range can be obtained by de-aggregation in the equivalent cluster.

[0159] The following description, using a specific application scenario, further illustrates the beneficial effects achievable in this embodiment. The equivalent cluster of a 21-node dual-port system comprises 3 micro gas turbines, 2 energy storage units, and 8 distributed photovoltaic systems, as shown in the topology diagram below. Figure 3 As shown. Daily distributed photovoltaic output as follows: Figure 5 As shown, the daily load is as follows Figure 6 As shown, the data for the micro gas turbine and energy storage unit are shown in Table 1 and Table 2, respectively.

[0160] Table 1. Parameters of micro gas turbines:

[0161]

[0162] Table 2. Energy Storage Unit Parameters:

[0163]

[0164] It should be noted that the corresponding program was written in the computational software MATLAB R2024a, which calls the Yalmip solver equipped with Gurobi for solving the problem. The computing device used to solve the optimization problem was a Legion laptop with an Intel Core i7-10510U processor, 32GB of RAM, and running Windows 11 Professional operating system.

[0165] It should be noted that, Figures 7 to 10 The first and second ports are shown as port 1 and port 2. The operating ranges of ports A and B obtained by aggregation are as follows: Figure 7 As shown, Figure 7 These are the aggregation execution ranges of the first port and the second port obtained through aggregation as provided in this embodiment. After step S20, the aggregation result is as follows: Figure 8 As shown, Figure 8This is a schematic diagram of the rectangles corresponding to the high-dimensional polyhedron model provided in this application embodiment for each hour, consisting of 24 rectangles.

[0166] It should be noted that after step S30, the obtained boundary values ​​are as follows: Figure 9 As shown, Figure 9 These are the maximum and minimum value points on each side of the rectangle obtained within each time period, as provided in the embodiments of this application; the red dots above indicate the boundary values ​​under the constraints of each port. Finally, after the cutting in step S40, the operating interval for each hour of the 24-hour day considering the interconnection characteristics of ports A and B is obtained as follows: Figure 10 As shown, Figure 10 This is an aggregated operation interval diagram for each time period within a 24-hour day, considering the interconnection characteristics of two ports, provided by the embodiments of this application. Scheduling can be implemented within this interval.

[0167] It should be noted that 10,000 scheduling instructions were generated using the Monte Carlo method within the aggregated operating range of the equivalent cluster, and all scheduling instructions were input into the equivalent cluster to test whether the equivalent cluster could operate normally. The results in Table 3 show that all 10,000 scheduling instructions could be successfully executed by the equivalent cluster, proving the feasibility of the proposed method. The aggregated operating range obtained using the two-port equivalent cluster aggregation method was used in power grid dispatching, and the results were compared with those without the equivalent cluster aggregation method. As shown in Table 4, using the equivalent cluster aggregation method can significantly reduce the number of variables and constraints to be considered in the equivalent cluster during dispatching; the number of constraints after aggregation is only 16.18% of that before aggregation. Therefore, aggregating the equivalent cluster can accelerate the dispatching operation of the power grid, reducing the solution time from 43 seconds to less than 2 seconds. Even considering the time required for aggregation, the time required using the aggregation method is still only 62.18% of the time required without the aggregation method. The results prove the effectiveness of the proposed method.

[0168] Table 3. Scheduling results for equivalent cluster operating intervals:

[0169]

[0170] Table 4. Scheduling results for equivalent cluster operating intervals:

[0171]

[0172] The dual-port equivalent cluster aggregation method provided in this embodiment effectively overcomes the limitations of traditional methods through a progressive optimization process from independent intervals to the introduction of coupling boundaries, and then to model segmentation.

[0173] First, by solving a robust min-max optimization problem with an objective function of 0, independent aggregation operating ranges were established for the two ports, and an initial high-dimensional polyhedron model was built, providing a benchmark for subsequent analysis. However, this is only a preliminary and loose boundary. Due to the lack of consideration for interconnection characteristics, there are situations where the two ports cannot be realized simultaneously within their respective aggregation operating ranges.

[0174] The key improvement lies in solving the power condition extrema of this port under the constraints of the other port's operating range. This step rigorously introduces physical coupling and operational constraints between the two ports, thereby correcting the independent and loose boundaries of each port into a series of power boundary points reflecting the interconnection relationship. This step significantly reduces complexity. The final step uses these points to cut the initial high-dimensional polyhedron model, eliminating infeasible operating spaces that exist due to neglecting coupling, ultimately quickly generating a relatively accurate and feasible target high-dimensional polyhedron model.

[0175] Compared with existing technologies, this application does not stop at the simple superposition of independent boundaries, but systematically solves the core problems of difficulty in solving aggregation models and low aggregation efficiency caused by the dual-port interconnection characteristics through a coherent technical chain of constraint introduction, boundary correction and model cutting, thereby achieving faster and more efficient cluster equivalent aggregation.

[0176] Based on the methods in the above embodiments, this application provides an embodiment of an electronic device. Please refer to... Figure 11 The electronic device 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 can invoke logical instructions stored in the memory to execute the methods described in the above embodiments.

[0177] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and 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 part 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, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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)).

[0183] It is understood that the various numerical designations used in the embodiments of this application are for descriptive convenience only and are not intended to limit the scope of the embodiments of this application.

[0184] 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 dual-port equivalent cluster aggregation method, characterized in that, include: The dual-port equivalent cluster is modeled based on the distributed energy resources set in the equivalent cluster, and the model constraints are obtained. Then, the aggregation problem is solved based on the model constraints to obtain the first aggregation operation interval of the first port and the second aggregation operation interval of the second port. The aggregation problem is a robust optimization problem with the goal of finding the aggregation operation interval of the dual-port equivalent cluster under all scheduling scenarios. Based on the first aggregation operation interval and the second aggregation operation interval, an initial high-dimensional polyhedron model is aggregated, and initial high-dimensional constraints are generated; Based on the model constraints and the initial high-dimensional constraints, the power boundary values ​​of each port under the constraints of the operating interval of the other port are obtained respectively. The initial high-dimensional polyhedron model is cut based on multiple power boundary values ​​to obtain a target high-dimensional polyhedron model for aggregating a dual-port equivalent cluster.

2. The dual-port equivalent cluster aggregation method as described in claim 1, characterized in that, The model constraints include: a first model constraint at the first port and a second model constraint at the second port; A dual-port equivalent cluster is modeled based on the distributed energy resources configured in the equivalent cluster, and model constraints are obtained. The aggregation problem is solved based on these constraints, and the first aggregation operating range of the first port and the second aggregation operating range of the second port are obtained respectively, including: The first model constraint is obtained based on the distributed energy and node position of the first port in the equivalent cluster. Then, the aggregation problem is solved based on the first model constraint to obtain the first aggregation operation range of the first port. The second model constraint is obtained based on the node positions of the distributed energy and the second port in the equivalent cluster. Then, the aggregation problem is solved based on the second model constraint to obtain the second aggregation operation range of the second port.

3. The dual-port equivalent cluster aggregation method as described in claim 2, characterized in that, Solving the aggregation problem based on the constraints of the first model includes: The aggregation problem is constructed as a robust min-max optimization problem with an objective function of 0, and the inner max problem is transformed into a dual min problem in multiple iterations. Solve the dual problem to obtain the worst scheduling instruction and the corresponding effective constraint indicator variable under the current iteration, and obtain the nearest feasible scheduling instruction based on the worst scheduling instruction; Based on the effective constraint indicator variable and the feasible scheduling instruction, the boundary constraints of the first aggregated running interval are iteratively updated, and the steps of solving the dual problem are executed until the solution of the dual problem is 0, so as to confirm that the current boundary constraints are the final result of the first aggregated running interval.

4. The dual-port equivalent cluster aggregation method as described in claim 1, characterized in that, The initial high-dimensional constraint is: ； ； ； Where Y is and The vector formed; This represents the exchange power between the first port and the power grid. This refers to the power exchanged between the second port and the power grid. yes and- The matrix formed; Total number of time periods for OK The identity matrix of columns; yes , , and The vector formed; This is the upper limit of the exchange power between the first port and the power grid; This is the lower limit of the exchange power between the first port and the power grid; This is the upper limit of the exchange power between the second port and the power grid; This is the lower limit of the exchange power between the second port and the power grid.

5. The dual-port equivalent cluster aggregation method as described in claim 1, characterized in that, Based on the model constraints and the initial high-dimensional constraints, the power boundary values ​​of each port under the constraints of the operating interval of the other port are obtained, including: Based on the model constraints and the initial high-dimensional constraints, the power boundary values ​​of the first port under the upper limit condition of the exchange power between the second port and the power grid and the lower limit condition of the exchange power between the second port and the power grid in each time period are obtained respectively. Based on the model constraints and the initial high-dimensional constraints, the power boundary values ​​of the second port are obtained under the upper limit of the exchange power between the first port and the power grid and the lower limit of the exchange power between the second port and the power grid in each time period.

6. The dual-port equivalent cluster aggregation method as described in claim 5, characterized in that, The model constraints are as follows: ； ； ； in, Y is a vector consisting of all decision variables; and The vector formed; This represents the exchange power between the first port and the power grid. This refers to the power exchanged between the second port and the power grid. , and For parameter matrices; , The coefficient vector is the parameter matrix; both the parameter matrix and the coefficient vector are determined by the operational constraints, output constraints, and network constraints of the equivalent cluster. The power boundary values ​​include the maximum and minimum exchange power; the maximum exchange power of the first port under the upper limit condition of the exchange power between the second port and the power grid is: ； ； ； in, For initial high-dimensional constraints; yes and- The matrix formed; Total number of time periods for OK The identity matrix of columns; yes , , and The vector formed; This is the upper limit of the exchange power between the first port and the power grid; This is the lower limit of the exchange power between the first port and the power grid; This is the upper limit of the exchange power between the second port and the power grid; This is the lower limit of the exchange power between the second port and the power grid; The relationship matrix for port switching power.

7. The dual-port equivalent cluster aggregation method as described in claim 1, characterized in that, The initial high-dimensional polyhedron model is segmented based on multiple power boundary values ​​to obtain a target high-dimensional polyhedron model for aggregating a dual-port equivalent cluster, including: Multiple two-dimensional polygon models are generated based on the multiple power boundary values ​​within each time period; The initial high-dimensional polyhedron model is cut based on multiple two-dimensional polygon models to generate cutting results; The cutting results are transformed into a target high-dimensional polyhedron model, and the target high-dimensional constraints are output to aggregate dual-port equivalent clusters.

8. An electronic device, characterized in that, Includes memory and one or more processors; The memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions; The one or more processors invoke the computer instructions to cause the electronic device to perform the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that: When the computer program or instructions are run on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1 to 7.

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