Multi-port power grid aggregation method based on cut plane projection

Abstract

The invention belongs to the technical field of power grid dispatching, and particularly discloses a multi-port power grid aggregation method based on cut plane projection. According to the method, the constraint is obtained by modeling the multi-port power grid based on the distributed energy, the robust optimization problem is solved to obtain the independent aggregation interval of each port, and the independent aggregation interval is integrated into the initial aggregation space. And solving a cutting plane problem, identifying an infeasible region, and obtaining a cutting plane constraint and a worst scheduling instruction. Then, according to these, a feasible scheduling instruction closest to the worst scheduling instruction in the current aggregation space is found, and the core function of the feasible scheduling instruction is to provide a reference point for the projection of each time period; and projecting a cutting plane constraint to each time period space by using the reference point, cutting and updating an aggregation space, and gradually cutting off an infeasible region by iterating the process. The interconnection characteristics among multiple ports are gradually and accurately depicted, the problem of difficult aggregation caused by lack of accurate depicting means in the prior art is solved, and compared with the prior art, the aggregation reliability is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power grid dispatching, and particularly relates to intelligent dispatching system technology, and more particularly to a multi-port power grid aggregation method based on cut plane projection. BACKGROUND

[0002] Currently, if the distributed energy in the access network architecture within a certain range is regarded as a power grid, and then through aggregation, it becomes a power grid interacting with the outside world together with a large number of distributed energy, the number of constraints and variables that need to be considered by the upper power grid in dispatching operation can be reduced, thereby greatly reducing the complexity of power system operation and improving the efficiency of distributed energy management.

[0003] However, the aggregation of the power grid also has the following problems: first, the aggregation of the power grid needs to consider the operating characteristics of different types of distributed energy and the network constraints, and under the premise that all dispatching instructions in the aggregation interval can be disaggregated in the power grid. Secondly, the division of the power grid is uncertain, unlike the main grid and the distribution network which have clear geographical area boundaries, the power grid is only a virtual unit composed of some distributed energy and its network architecture structure which has physical boundaries in the distribution network. As shown in FIG. 1, when it is in the distribution network, it often forms a multi-port, and the number of ports connected with the outside power grid is ≥3. How to consider the interconnection characteristics between the multi-ports in the aggregation is also a challenge of the current technology. Figure 1 SUMMARY

[0004] In view of the defects of the prior art, the purpose of the present application is to provide a multi-port power grid aggregation method based on cut plane projection, which aims to solve the problem that in the current multi-port power grid aggregation scenario, there is a lack of accurate description means for the interconnection characteristics between the multi-ports, so that the aggregation is difficult.

[0005] ​The first aspect of the present application relates to a multi-port power grid aggregation method based on a cut plane projection, comprising: step S10, modeling a multi-port power grid based on distributed energy sources arranged in the power grid and obtaining model constraints, and then solving an aggregation problem based on the model constraints to obtain a plurality of independent aggregation operating intervals of a plurality of ports; the aggregation problem is a robust optimization problem aiming to find the aggregation operating intervals of the multi-port power grid under all scheduling scenarios; step S20, integrating the plurality of independent aggregation operating intervals into an initial aggregation space of the multi-port power grid, and determining the initial aggregation space as a current aggregation space; step S30, solving a cut plane acquisition problem based on the current aggregation space and the model constraints to obtain a current cut plane constraint and a current worst scheduling instruction; the cut plane acquisition problem is a robust optimization problem aiming to test the feasibility of the current aggregation space and generate a cut plane constraint for cutting off an infeasible region; the current worst scheduling instruction refers to an infeasible scheduling instruction with the maximum sum of power correction values of all time periods among all originally infeasible scheduling instructions that become feasible after correction under the current aggregation space; step S40, obtaining a current feasible scheduling instruction closest to the current worst scheduling instruction under the current aggregation space according to the current worst scheduling instruction and the current cut plane constraint; step S50, projecting the current cut plane constraint to the current aggregation space under different time periods under the constraint of the current closest feasible scheduling instruction to cut and update the current aggregation space, and then executing the step of solving the cut plane acquisition problem based on the current aggregation space and the model constraints until the current aggregation space is feasible, thereby realizing the aggregation of the multi-port power grid.

[0006] In an embodiment, the cut plane acquisition problem is modeled as a max-min robust optimization problem with an objective function containing the model constraints and the current aggregation space constraints , wherein, is a column vector consisting entirely of integer 1; is a vector corresponding to the upward power correction value, is a vector corresponding to the downward power correction value.

[0007] In an embodiment, step S30 comprises: performing dual transformation on the inner min problem of the cut plane acquisition problem to obtain a dual problem of the cut plane acquisition problem; solving the dual problem to obtain the current worst scheduling instruction under the current aggregation space and the dual variable solution value; and generating the current cut plane constraint under the current aggregation space based on the objective of the dual problem being less than or equal to 0, in combination with the current worst scheduling instruction and the dual variable solution value.

[0008] In an embodiment, step S40 comprises: determining the non-cut plane constraint of the current feasible scheduling instruction based on the current cut plane constraint; determining the power grid constraint of the current feasible scheduling instruction based on the model constraint; and obtaining the current feasible scheduling instruction closest to the current worst scheduling instruction under the current aggregation space based on the non-cut plane constraint and the power grid constraint.

[0009] In an embodiment, the step S50 comprises: decomposing and projecting the current cut plane constraint by time periods to obtain a projection cut plane constraint for each time period; wherein the projection cut plane constraint for each time period ensures that its geometry is consistent with the shape of the original cut plane in the time period and passes through the component of the latest current feasible schedule instruction in the time period; integrating the projection cut plane constraints of all time periods into an overall projection cut plane constraint covering the whole time period; combining the overall projection cut plane constraint with the current aggregate space constraint to generate a new aggregate space constraint with a narrowed cutting range, thereby completing the update of the current aggregate space.

[0010] In an embodiment, the projection cut plane constraint for each time period ensures that its geometry is consistent with the shape of the original cut plane in the time period and passes through the component of the latest current feasible schedule instruction in the time period, comprising: based on the dual variable solution value and the current feasible schedule instruction, decomposing the parameters of the current cut plane constraint by time periods to extract a parameter slice corresponding to each time period; wherein the calculation of the parameter slice incorporates the time period component of the latest current feasible schedule instruction and then performs offset adjustment, thereby constructing an independent projection cut plane constraint for each time period.

[0011] In an embodiment, the iterative process converges until the current aggregate space is feasible, which is a condition that the cut plane acquisition problem result is zero, indicating that all schedule instructions of the current aggregate space are executable.

[0012] In a second aspect, the present application provides an electronic device, comprising: at least one memory configured to store a program; and at least one processor configured to execute the program stored in the memory, and when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation manner of the first aspect.

[0013] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, and when the computer program is run on a processor, the processor executes the method described in the first aspect or any possible implementation manner of the first aspect.

[0014] In a fourth aspect, the present application provides a computer program product, and when the computer program product is run on a processor, the processor executes the method described in the first aspect or any possible implementation manner of the first aspect.

[0015] In general, the above technical solutions conceived by the present application have the following beneficial effects compared with the prior art: The application models a multi-port power grid based on a distributed energy source, acquires model constraints, and then solves a robust optimization problem to find an aggregated operating interval under all scheduling scenarios, to obtain an independent aggregated operating interval for each port, thereby initially and accurately depicting the operating characteristics of a single port and laying the foundation for aggregation. Then, the independent intervals are integrated into an initial aggregation space as the current aggregation space to preliminarily reflect the rudiment of multi-port interconnection.

[0016] Then, by solving a cutting plane acquisition problem with the goal of testing the feasibility of the current aggregation space and generating a cutting plane constraint, the current cutting plane constraint and the worst scheduling instruction are acquired, so as to identify the infeasible region in the aggregation space and prepare for cutting. Then, according to the worst scheduling instruction and the cutting plane constraint, the nearest feasible scheduling instruction of the current aggregation space to the worst scheduling instruction is acquired, to provide accurate positioning for subsequent projection. Finally, under the constraint of the nearest feasible scheduling instruction, the cutting plane constraint is projected to the current aggregation space in different time periods, the current aggregation space is cut and updated, and the cutting plane acquisition problem is iteratively executed until the aggregation space is feasible. This series of technical means iteratively removes infeasible regions and retains feasible points, gradually and accurately depicting the interconnection characteristics between multiple ports, solving the aggregation difficulty problem caused by the lack of accurate depiction means in the prior art, and realizing efficient and reliable aggregation of the multi-port power grid compared with the prior art, improving the feasibility of the scheduling instruction and the stability of the power grid operation. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a conceptual diagram of the multi-port power grid interacting with the upper-level power grid provided by the embodiment of the application; Figure 2 is a flowchart of a multi-port power grid aggregation method based on cutting plane projection provided by the embodiment of the application; Figure 3 is a topology diagram of a power grid based on a 30-node system provided by the embodiment of the application; Figure 4 is a diagram of the output of each distributed photovoltaic power in a day provided by the embodiment of the application; Figure 5 is a diagram of the total load in the power grid provided by the embodiment of the application; Figure 6 is a diagram of the operating interval of each of the A, B, and C ports obtained by aggregation provided by the embodiment of the application; Figure 7 is a diagram of the geometric representation corresponding to the initial value of the multi-port aggregation space in each hour of the day obtained by aggregation provided by the embodiment of the application; Figure 8 is a representation diagram of the time period 10 cut by the projected cutting plane in the first iteration provided by the embodiment of the application; Figure 9 is a running interval diagram considering A, B, and C three-port interconnection characteristics per hour in 24 hours of a day obtained by the aggregation provided in the embodiments of the present application. Figure 10 is a structural schematic diagram of an electronic device provided in the embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0019] In the present application, the term "and / or" is used to describe the association relationship between the associated objects, which means that there can be three kinds of relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In the present application, the symbol " / " represents the relationship of or, for example, A / B represents A or B.

[0020] In the present application, the terms "first" and "second" are used to distinguish different objects, rather than to describe the specific order of the objects. For example, the first response message and the second response message are used to distinguish different response messages, rather than to describe the specific order of the response messages.

[0021] In the embodiments of the present application, the words "exemplary" or "for example" are used to represent an example, illustration or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words "exemplary" or "for example" are used to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of the present application, unless otherwise specified, "a plurality of" means two or more, for example, a plurality of processing units means two or more processing units, and a plurality of elements means two or more elements.

[0023] At present, in the multi-port power grid aggregation scenario, there is a problem of aggregation difficulty due to the lack of accurate means for describing the interconnection characteristics between multi-ports. Based on this, the present application proposes a multi-port power grid aggregation method based on cut plane projection. Please refer to Figure 2 , Figure 2 is a flowchart of a multi-port power grid aggregation method based on cut plane projection provided in the embodiments of the present application.

[0024] In the present embodiment, the multi-port power grid aggregation method based on cut plane projection includes steps S10 to S50.

[0025] Step S10: Model the multi-port power grid based on the distributed energy resources set in the power grid and obtain the model constraints. Then solve the aggregation problem based on the model constraints to obtain multiple independent aggregation operation intervals for multiple ports.

[0026] It should be noted that multi-port grid modeling based on distributed energy resources within the power grid refers to constructing a mathematical model that can characterize the operational characteristics of each distributed energy resource within the power grid and its coupling relationship with multi-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 micro gas turbines, the charging and discharging power and capacity limits of energy storage, and the output prediction range of photovoltaics, as well as network constraints of port connection points determined by the external power grid, such as voltage safety limits.

[0027] by Figure 1 For example, Figure 1 This is a conceptual diagram illustrating the interactive power operation between a multi-port power grid and its upstream power grid, as provided in an embodiment of this application. Figure 1 In the grid, the components include micro gas turbines, energy storage units, and distributed photovoltaics; 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 the current time period, For nodes.

[0028] Based on the above definitions, the following constraints are established to represent the restrictive relationships defined above: (1); (2); (3); It should be noted that equations (1) to (3) are the operating constraints of the micro gas turbine.

[0029] (4); (5); (6); (7); It should be noted that equations (4) to (7) are the operating constraints of the energy storage unit.

[0030] (8); (9); It should be noted that formula (8) and (9) are output constraints of distributed photovoltaic.

[0031] (10); (11); (12); (13); (14); (15); It should be noted that formula (10) to (15) are network constraints.

[0032] Specifically, is the upper and lower limit of the power output of the micro gas turbine, is the upper limit of the power climb and the lower limit of the power climb of the micro gas turbine; is the upper limit of the power generation and the upper limit of the energy storage of the energy storage unit, is the upper and lower limit of the energy storage, is the self-discharge rate of the energy storage unit; is the power generation of the distributed photovoltaic, is the upper limit of the light rejection rate of the distributed photovoltaic; is the active / reactive load, is the upper and lower limit of the active power of the transmission line, is the upper and lower limit of the reactive power of the transmission line, is the upper and lower limit of the node voltage square, is the resistance and inductance of the transmission line; is the phase angle of the micro gas turbine, the energy storage unit and the distributed photovoltaic respectively. The superscript max represents the upper limit value, and the superscript min represents the lower limit value.

[0033] It should be noted that the model constraints of the above power grid can be arranged in the following compact form: (16); (17); (18).

[0034] It can be understood that, is a vector composed of all decision variables, is the size of the total power exchanged between the n ports and the upper-level power grid; formula (16) corresponds to all inequality constraints, and formula (17) corresponds to all equality constraints; the parameter matrix and the coefficient vector are determined by each constraint; is the port total power exchanged with the upper-level grid, wherein Equation (18) is a constraint corresponding to the aggregated operating region of the grid, is and port exchange power. For clear definition, the superscript T represents transposition, and all other symbols represent the total number of time periods.

[0035] Specifically, and correspond to , is the upper and lower bounds of the grid port output power, and the corresponding relationship is as follows: Equation (19); wherein, is a unit matrix of rows columns.

[0036] It should be noted that the aggregated problem is a robust optimization problem aimed at finding the aggregated operating region of the multi-port grid under all scheduling scenarios. The aggregated operating region refers to the set of all possible operating points of the two ports of the grid under the premise of meeting all internal operating constraints.

[0037] It can be understood that for the grid, the scheduling instruction given by the external grid is uncertain. At the same time, the scheduling instruction given by the external grid is made according to the aggregated operating region submitted by the multi-port grid. Therefore, we hope to find an aggregated operating region, so that no matter what scheduling instruction the external grid makes according to the aggregated operating region, the scheduling instruction can be successfully executed by the grid.

[0038] Therefore, it can be understood that the robust optimization problem proposed in the present application is a decision optimization problem, and the goal is not to maximize or minimize a certain performance index under the consideration of the uncertainty of the grid, but to determine the feasibility of the aggregated operating region of the grid. The core difference between the mathematical form of the traditional economic dispatch or optimal power flow problem is that the objective function is constructed as a constant, for example, 0, and the task of the solver is to search for the boundary of the feasible solution space that makes the problem solvable, rather than to find a single optimal solution.

[0039] Specifically, the aggregated problem can be implemented by a min-max robust optimization problem with an objective function of 0. The min-max robust optimization problem is an optimization method aimed at finding a feasible decision scheme under the worst-case scenario of uncertainty; in this context, solving with an objective function of 0 aims to focus on searching for a feasible power boundary that satisfies all model constraints, rather than pursuing a specific economic target.

[0040] It should be noted that the aggregation problem can be expressed in the following form: (20).

[0041] It is understandable that RES refers to the aggregation problem. Based on the above model constraints and aggregation runtime constraints, i.e., taking equations (16) to (18) as constraints, equation (20) 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 equation (20) is... .

[0042] Specifically, although the overall model expression is consistent, the topology, line parameters and electrical distances of the power grid are different due to the differences in the physical location (i.e., nodes) of multiple ports. As a result, the key parameters in the model constraints involved in solving the maximum and minimum operating capabilities of each port individually will actually change.

[0043] Therefore, during modeling and optimization, model constraints need to be explicitly divided into first-port, second-port, third-port, etc., to ensure that the aggregated operating range calculation for each port accurately reflects the specific electrical environment of its node. From an implementation perspective, this can be accomplished through a constraint management module configured in the energy management system: this module is server-based in hardware and integrates a power grid model database in software. It can automatically call the corresponding parameter set based on the node identifier connected to the port, and generate and load the model constraints for the first-port, second-port, third-port, etc., into the optimization solver.

[0044] Understandably, this method of dividing model constraints by port fundamentally ensures that the constraints relied upon when solving the aggregation problem step by step are consistent with the actual physical connection of each port. This makes the obtained aggregation operating ranges of the first port, second port, third port, etc. not only mathematically rigorous and feasible, but also able to 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 instructions.

[0045] Specifically, the solution process for the first port is given here; the solution process for the aggregation runtime of the remaining ports can be obtained similarly. To make the original min-max problem easier to solve, the inner max problem in the aggregation problem is transformed into duality, resulting in the... A unified min dual problem in the next iteration is as follows: (twenty one); (twenty two); (23).

[0046] Specifically, is a dual variable, is a Lagrange multiplier for KKT conditions, is a sufficiently large positive number in the "big M" method, is a Boolean variable, is a parameter matrix, is the total number of time periods, is Row Column identity matrix.

[0047] Specifically, solving equation (21) can obtain the worst scheduling instruction under the current and the effective constraint indicated by the Boolean variable The value (0 or 1) of the Boolean variable directly defines and indicates whether the inequality constraint corresponding to it is an effective constraint. The complementary relaxation condition in the KKT condition (embodied as constraints and the sign restriction on λ and the relaxation amount in the problem) forms a mathematical linkage with the value of =0 (effective constraint) and λ>0 occur at the same time, while =1 (ineffective constraint) and λ=0 occur at the same time. Therefore, under this modeling framework, the binary variable is a decision variable specially designed to identify effective constraints, and the two are equivalent.

[0048] Then, the latest feasible scheduling instruction is obtained using the worst scheduling instruction as follows: (24); (25).

[0049] Specifically, according to and the feasible scheduling instruction , iterate as follows: (26); (27).

[0050] Specifically, is a Boolean variable, is a vector with the same dimension as ; let and repeat all steps of solving the aggregation problem until .

[0051] Finally, the multiple-port independent aggregated operating intervals can be obtained. However, in the actual interconnection process, the two are necessarily interdependent, and thus the ability of each exchange as a whole should be less than this limit.

[0052] Step S20, integrating the multiple independent aggregated operating intervals into an initial aggregated space of the multi-port power grid, and determining the initial aggregated space as the current aggregated space.

[0053] It should be noted that integration refers to a composition operation, and the input is the multiple independent aggregated operating intervals obtained in step S10, which are defined respectively. These operating intervals can be mathematically represented as convex polyhedrons. The initial high-dimensional polyhedral model is a higher-dimensional geometric model constructed by combining the polyhedral operating intervals of the two ports mentioned above. This model completely describes the set of solutions of the external interaction power of the power grid in multiple time periods without considering the interconnection factors.

[0054] It should be noted that the aggregated operating intervals of all ports are integrated into a high-dimensional polyhedron represented by H, that is, the initial aggregated space: (28); (29).

[0055] It should be noted that is a unit matrix of rows and columns; is the total number of ports; is the total number of time periods; the high-dimensional polyhedron is the initial value of the multi-port aggregated space, which corresponds to a -dimensional polyhedron in each time period; each axis of the space where the -dimensional polyhedron is located corresponds to the power of a port, each face of the -dimensional polyhedron corresponds to the component in the axis direction. Therefore , can be represented by equation (29).

[0056] It can be understood that the initial aggregated space is used as the current aggregated space as an initial condition for the subsequent step operation in the initial state. This initial aggregated space does not consider any interconnection characteristics between ports and only expresses the maximum and minimum power range of each port.

[0057] Step S30, solving the cutting plane acquisition problem based on the current aggregated space and the model constraints to obtain the current cutting plane constraints and the current worst scheduling instruction.

[0058] It should be noted that the cut plane acquisition problem refers to a problem of solving, through mathematical optimization, whether there is an infeasible dispatch instruction in the current aggregation space in the iteration process of multi-port grid aggregation. The target is to find the cut plane where the worst dispatch instruction is located, to cut the infeasible region, and gradually optimize the aggregation space. Finally, it is ensured that the aggregation space is feasible under all dispatch scenarios, which is the key to iteration convergence.

[0059] It should be noted that the dispatch instruction refers to the arrangement of power values in each period; and the current worst dispatch instruction refers to an infeasible dispatch instruction with the maximum sum of power correction values in all periods among all originally infeasible dispatch instructions that become feasible after correction under the current aggregation space.

[0060] Therefore, the cut plane acquisition problem is a robust optimization problem aiming to test the feasibility of the current aggregation space and generate a cut plane constraint for cutting off the infeasible region.

[0061] In a feasible implementation, the cut plane acquisition problem is modeled as a max-min robust optimization problem with a target function of and model constraints and current aggregation space constraints.

[0062] Specifically, it is modeled as a max-min problem as follows: (30). (31).

[0063] Specifically, is a vector corresponding to the upward power correction value, is a vector corresponding to the downward power correction value, is a column vector consisting of all 1s, is a correction value that makes the dispatch instruction feasible. and are grid constraints, in which the correction value is considered; is the multi-port aggregation space in the current iteration, m is the iteration sequence number, and m is 1 in the initial state, i.e., the initial aggregation space in the foregoing; is to limit both power correction values to be positive.

[0064] It can be understood that since the dispatch instruction is made according to the multi-port aggregation space in the current iteration , there may be an infeasible part, so the power correction value is used to make it feasible, and when the result of formula (30) is 0, it means that no matter what dispatch instruction is made, it will not need a power correction value to make it feasible, which means that the iteration has reached a feasible multi-port aggregation space.

[0065] That is, the iterative process converges when the current aggregated space is feasible, i.e., the result of the cut plane acquisition problem is zero, which means that all the dispatch instructions in the current aggregated space are executable.

[0066] To implement step S30, first, the inner min problem of the cut plane acquisition problem is dual transformed to obtain the dual problem of the cut plane acquisition problem. The obtained unified max dual problem is as follows: (32); (33).

[0067] wherein, is a dual variable, is a bilinear term, which is processed equivalently by using the KKT condition as follows: (34); (35).

[0068] wherein, is a KKT multiplier, is a Boolean variable.

[0069] Then, solving the dual problem can obtain the current worst dispatch instruction under the current multi-port aggregated space and the solution value of the dual variable .

[0070] Finally, based on the objective that the result of the dual problem is less than or equal to 0, the current cut plane constraint of the current aggregated space is generated in combination with the current worst dispatch instruction and the solution value of the dual variable.

[0071] It should be noted that when the algorithm has found a perfect aggregated space, any dispatch instruction Y does not need any power correction to meet all the internal constraints of the power grid. At this time, the optimal value of the inner min problem is 0, so the result of the entire max-min problem . The algorithm is successful, and can be stopped.

[0072] It can be understood that in the middle step of the algorithm, the current aggregated space is not perfect, and there are some infeasible dispatch instructions. For these instructions, the power grid must pay an additional cost, i.e., a power correction value, to operate normally. The outer max will solve the infeasible dispatch instruction that needs the maximum correction cost, i.e., the worst dispatch instruction . At this time, represents the sum of the maximum correction cost. is an alarm that clearly indicates that there is an infeasible region in the current aggregated space.

[0073] It can be understood that, for formula (32), considering that the original max-min problem is solved to a feasible multi-port aggregation space, ; in the iteration process, since the power correction value is needed ; considering strong duality, in the dual problem is equivalent to , so the cut plane of this part of the infeasible space can be cut off, corresponding to .

[0074] At this time, the cut plane in the multi-port aggregation space obtained by the th iteration is as follows: (36).

[0075] It can be understood that represents that the left new symbol, variable or expression is defined by the right expression; formula (36) is constructed by the solution value of the dual variable to express the infeasible region cut off by the cut plane .

[0076] Step S40, according to the current worst scheduling instruction and the current cut plane constraint, obtaining the current feasible scheduling instruction closest to the current worst scheduling instruction under the current aggregation space.

[0077] It can be understood that, step S40 includes: determining the non-cut plane constraint of the current feasible scheduling instruction based on the current cut plane constraint; determining the power grid constraint of the current feasible scheduling instruction based on the model constraint; and obtaining the current feasible scheduling instruction closest to the current worst scheduling instruction under the current aggregation space based on the non-cut plane constraint and the power grid constraint.

[0078] It can be understood that this non-cut plane constraint means that the solution value does not fall within the part to be cut off by the cut plane.

[0079] Specifically, the solution process is: (37); (38).

[0080] Wherein, formula (37) is to solve the objective function of the current feasible scheduling instruction in the two norm distance, the constraint , is to indicate that is a scheduling instruction feasible for the power grid constraint, and the constraint is to make located the cut range.

[0081] It can be understood that the closest current feasible dispatch instruction is solved in order to realize accurate positioning along the time period segmentation.

[0082] In step S50, under the constraint of the current closest feasible dispatch instruction, the current cut plane constraint is projected to the current aggregated space under different time periods to cut and update the current aggregated space, and then the step of solving the cut plane acquisition problem based on the current aggregated space and the model constraint is executed until the current aggregated space is feasible, realizing multi-port power grid aggregation.

[0083] It can be understood that the projection specifically decomposes the global cut plane constraint describing the entire dispatch period, such as 24 hours, into a set of local constraints describing each time period, such as the tth hour. The technical essence is dimension reduction and decoupling. Through projection, the complex constraint coupled with the port power of all time periods is converted into a set of simple constraints independent of each other, which only involves the port power of each time period in each time period. This significantly reduces the complexity of subsequent calculations.

[0084] In addition, the multi-port aggregated space is actually the aggregation of the coupling characteristics between the ports. To some extent, the coupling characteristics are determined by the network inside the power grid, and the network constraint inside the power grid is time-periodic. The cut plane projection can better cope with the time-periodic situation.

[0085] It can be understood that the cut refers to adding the linear inequality constraint, i.e., the set of projected cut planes of each time period, as a new constraint condition to the constraint set of the current aggregated space. The update refers to defining the aggregated space used in the next iteration with this new and more stringent constraint set. The new space is a subset of the original space, and those regions that are judged to be infeasible are cut out.

[0086] In a feasible implementation, the current cut plane constraint is first decomposed and projected according to different time periods to obtain independent projected cut plane constraints for each time period; wherein the projected cut plane constraint of each time period ensures that its geometric shape is consistent with the shape of the original cut plane in that time period, and must pass through the component of the current feasible dispatch instruction in that time period.

[0087] Specifically, the parameters of the current cut plane constraint can be time-decomposed based on the dual variable solution value and the current feasible dispatch instruction to extract the parameter slice corresponding to each time period; wherein the calculation of the parameter slice incorporates the time component of the current feasible dispatch instruction, and uses Kronecker product operation for offset adjustment, so as to construct the independent projected cut plane constraint of each time period, forcing it to pass through the feasible point and maintaining the geometric projection consistency with the original cut plane in that time period.

[0088] Therefore, the cut plane is projected to each time interval , and the projection cut plane is obtained in the space of the polytope of each time interval as follows:

[0089] wherein, represents the element from the th element to the th element of the vector in the bracket; is the Kronecker product.

[0090] It can be understood that the matrix obtained by formula (39) is to make the shape of the projection cut plane of each time interval consistent with the cut plane before projection in the time interval; and the vector is to make the projection cut plane of each time interval pass through the component of the feasible dispatch instruction in the time interval.

[0091] Then, the projection cut plane constraints of all time intervals are integrated into the overall projection cut plane constraint covering the whole time interval. The projection cut plane of all time intervals is integrated into the following form: (40).

[0092] It can be understood that, through formula (40), the integrated form of all projection cut planes can be obtained.

[0093] Finally, the overall projection cut plane constraint is combined with the current aggregated space constraint to generate a new aggregated space constraint with the range of the cut being reduced, so as to complete the update of the current aggregated space.

[0094] It can be understood that the multi-port aggregated space after the iteration of the projection cut plane is as follows: (41).

[0095] It should be noted that, then let , and the step of solving the cut plane acquisition problem based on the current aggregated space and the model constraint is executed again until the current aggregated space is feasible, that is, , and the multi-port power grid aggregation is realized.

[0096] In the embodiment, by modeling the multi-port power grid based on distributed energy and obtaining model constraints, a robust optimization problem is solved to find the aggregated operating interval under all scheduling scenarios, and the independent aggregated operating interval of each port is obtained, thereby preliminarily and accurately depicting the operating characteristics of a single port and laying a foundation for aggregation; then, the independent intervals are integrated into an initial aggregation space as the current aggregation space to preliminarily reflect the rudiment of multi-port interconnection.

[0097] Then, by solving a cutting plane acquisition problem with the objective of testing the feasibility of the current aggregation space and generating a cutting plane constraint, the current cutting plane constraint and the worst scheduling instruction are obtained, thereby identifying the infeasible region in the aggregation space and preparing for cutting; further, according to the worst scheduling instruction and the cutting plane constraint, the nearest feasible scheduling instruction of the current aggregation space to the worst scheduling instruction is obtained to provide accurate positioning for subsequent projection.

[0098] Finally, under the constraint of the nearest feasible scheduling instruction, the cutting plane constraint is projected to the current aggregation space in different time periods, the current aggregation space is cut and updated, and the cutting plane acquisition problem is iteratively executed until the aggregation space is feasible. This series of technical means gradually and accurately depict the interconnection characteristics between multiple ports by iteratively cutting off infeasible regions and retaining feasible points, thereby solving the aggregation difficulty problem caused by the lack of accurate depiction means in the prior art, and realizing efficient and reliable aggregation of the multi-port power grid compared with the prior art, thereby improving the feasibility of the scheduling instruction and the stability of the power grid operation.

[0099] The beneficial effects that can be achieved by the embodiment will be further described below in combination with a specific application scenario. For a 30-node three-port system of the power grid, it contains two micro gas turbines, two energy storage units, eight distributed photovoltaics, and ports A, B and C are located on nodes 1, 21 and 30 respectively. The topology diagram is as shown in Figure 3 , the daily distributed photovoltaic output is as shown in Figure 4 , the daily load is as shown in Figure 5 , and the data of the micro gas turbine and the energy storage unit are shown in Table 1 and Table 2 respectively: Table 1: Micro gas turbine parameters

[0100] Table 2: Energy storage unit parameters

[0101] The corresponding program is written on the calculation software MATLAB R2024a, and the Yalmip solver with Gurobi is called to solve. The calculation device applied to solve the optimization problem is a Legion Rescuer notebook computer with Intel Core i7-10510U processor, 32 GB running memory, and Windows 11 Professional operating system. Figure 6

[0102] It should be noted that the aggregation of the aggregation operation interval of A, B and C three ports can obtain a high-dimensional polyhedron , which is the initial value of the multi-port aggregation space. As shown in Figure 7 , the cube in each subgraph is the initial multi-port aggregation space in each time period, and the unit of each coordinate axis is MW.

[0103] It should be noted that the schematic diagram of the projection cut plane is shown in Figure 8 , and the unit of each coordinate axis is MW. The projection cut plane is the cut plane of the first iteration, i.e. , cutting the initial multi-port aggregation space , and the initial multi-port aggregation space is the grass green cuboid before cutting. The specific form of the projection cut plane at (42).

[0104] , where is the power of A, B and C ports at . The red plane is the projection cut plane, the blue polyhedron is the infeasible part cut out, and the grass green polyhedron below is the space corresponding to . The projection cut plane at can be similarly obtained.

[0105] It should be noted that the aggregation of the aggregation operation interval of A, B and C three ports in 24 hours per day is shown in Figure 9 , and the three-dimensional polyhedron in each subgraph is the aggregation operation interval, and the unit of each coordinate axis is MW. 10000 scheduling instructions are generated in the aggregation operation interval of the power grid, and all scheduling instructions are input into the power grid to test whether the power grid can run normally.

[0106] Table 3: Dispatching results of power grid operation interval

[0107] ​​The results in Table 3 show that 10000 dispatching instructions can be successfully executed by the power grid, proving the feasibility of the method. The aggregated operating interval obtained by the multi-port power grid aggregation method is used for power grid dispatching, and the dispatching without using the power grid aggregation method is compared.

[0108] Table 4: Power grid operating interval dispatching results

[0109] As shown in the results in Table 4, the number of variables and constraints in the power grid that need to be considered during dispatching can be significantly reduced, thereby accelerating the dispatching and operation of the power grid, reducing the solving time from more than 1 min to less than 10 s, proving the effectiveness of the method.

[0110] Based on the method in the above embodiment, as shown in Figure 10 The electronic device provided in the embodiment of the present application can include a processor, a communications interface, a memory and a communications bus, wherein the processor, the communications interface and the memory can complete mutual communication through the communications bus. The processor can invoke logical instructions in the memory to execute the method in the above embodiment.

[0111] In addition, the logical instructions in the memory described above can be implemented in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can essentially or say the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application.

[0112] Based on the method in the above embodiment, the embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program. When the computer program runs on the processor, the processor executes the method in the above embodiment.

[0113] Based on the method in the above embodiment, the embodiment of the present application provides a computer program product, and when the computer program product runs on the processor, the processor executes the method in the above embodiment.

[0114] It can be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0115] The method steps in the embodiments of the present application can be implemented in the form of hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in a random access memory (RAM), a flash memory, a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM), a register, a hard disk, a mobile hard disk, a CD-ROM or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor, so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC.

[0116] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of 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 the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in or transmitted by a 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 through a wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media sets. The available media can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0117] It can be understood that various numerical numbers involved in the embodiments of the present application are only distinguished for convenience of description, and are not used to limit the scope of the embodiments of the present application.

[0118] Those skilled in the art easily understand that the above only describes the preferred embodiments of the present application and is not used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A multi-port power grid aggregation method based on cut-plane projection, characterized in that, The method comprises the following steps: Step S10, modeling a multi-port power grid based on distributed energy sources arranged in the power grid and obtaining model constraints, and then solving an aggregation problem based on the model constraints to obtain a plurality of independent aggregation operation intervals of a plurality of ports respectively; the aggregation problem is a robust optimization problem aiming to find aggregation operation intervals of the multi-port power grid under all scheduling scenarios; Step S20, integrating the plurality of independent aggregation operation intervals into an initial aggregation space of the multi-port power grid, and determining the initial aggregation space as a current aggregation space; Step S30, solving a cutting plane acquisition problem based on the current aggregation space and the model constraints to obtain a current cutting plane constraint and a current worst scheduling instruction; the cutting plane acquisition problem is a robust optimization problem aiming to test the feasibility of the current aggregation space and generate a cutting plane constraint for cutting off an infeasible region; the current worst scheduling instruction refers to an infeasible scheduling instruction with the maximum sum of power correction values of all time periods among all originally infeasible scheduling instructions that become feasible after correction under the current aggregation space; Step S40, obtaining a current feasible scheduling instruction closest to the current worst scheduling instruction under the current aggregation space according to the current worst scheduling instruction and the current cutting plane constraint; Step S50, projecting the current cutting plane constraint to the current aggregation space under different time periods under the constraint of the current closest feasible scheduling instruction to cut and update the current aggregation space, and then performing the step of solving the cutting plane acquisition problem based on the current aggregation space and the model constraints until the current aggregation space is feasible, thereby realizing the aggregation of the multi-port power grid.

2. The cut-plane projection based multi-port power aggregation method of claim 1, wherein, The cut plane acquisition problem is modeled as a max-min robust optimization problem with an objective function including the model constraints and current aggregated space constraints wherein is a column vector consisting of all integer 1s; is a vector of up power correction values, is a vector of down power correction values.

3. The cut-plane projection based multi-port power aggregation method of claim 2, wherein, Step S30 comprises: performing dual transformation on an inner min problem of the cutting plane acquisition problem to obtain a dual problem of the cutting plane acquisition problem; solving the dual problem to obtain the current worst scheduling instruction and a dual variable solution value under the current aggregation space; generating the current cutting plane constraint under the current aggregation space based on a target of the dual problem result being less than or equal to 0, in combination with the current worst scheduling instruction and the dual variable solution value.

4. The cut-plane projection based multi-port power aggregation method of claim 1, wherein, Step S40 comprises: determining a non-cutting plane constraint of the current feasible scheduling instruction based on the current cutting plane constraint; determining a power grid constraint of the current feasible scheduling instruction based on the model constraint; obtaining the current closest feasible scheduling instruction under the current aggregation space based on the non-cutting plane constraint and the power grid constraint.

5. The cut-plane projection based multi-port power aggregation method of claim 1, wherein, Step S50 comprises: decomposing and projecting the current cutting plane constraint according to different time periods to obtain an independent projected cutting plane constraint of each time period; wherein the projected cutting plane constraint of each time period ensures that its geometric shape is consistent with the shape of the original cutting plane in the time period, and is subjected to the component of the closest current feasible scheduling instruction in the time period; integrating the projected cutting plane constraints of all time periods into an overall projected cutting plane constraint covering all time periods; combining the overall projected cutting plane constraint with the current aggregation space constraint to generate a new aggregation space constraint with a reduced range, thereby completing the update of the current aggregation space.

6. The cut-plane projection based multi-port power aggregation method of claim 5, wherein, The projection cut plane constraint of each time period ensures its geometry consistent with the shape of the original cut plane in the time period, and passes through the component of the nearest current feasible schedule instruction within the time period, including: Based on the dual variable solution value and the current feasible schedule instruction, the parameters of the current cut plane constraint are time-decomposed, and the corresponding parameter slice of each time period is extracted; wherein the calculation of the parameter slice incorporates the time component of the nearest current feasible schedule instruction, and then performs offset adjustment, thereby constructing an independent projection cut plane constraint for each time period.

7. The cut-plane projection based multi-port power aggregation method of claim 1, wherein, The iterative process continues until the convergence condition of the current aggregated space feasibility is met: the cut plane acquisition problem result is zero, indicating that all schedule instructions in the current aggregated space are executable.

8. An electronic device, comprising: comprise one or more processors; The memory is coupled with the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions; The one or more processors invoke the computer instructions to cause the electronic device to perform the method of any one of claims 1 to 7.

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

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

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