Dual-port microgrid p-q aggregation domain and aggregated cost surface rolling scheduling method

Abstract

The application discloses a double-port micro-grid P-Q aggregation domain and an aggregation cost curved surface rolling scheduling method. The method comprises the following steps: establishing a double-port micro-grid model based on the distributed energy running state of the micro-grid in a current scheduling period; constructing an initial P-Q aggregation domain of the micro-grid, and cutting the initial P-Q aggregation domain through an iterative solving mode based on the double-port micro-grid model until a convergent feasible P-Q aggregation domain is obtained; selecting a plurality of P-Q sampling points in the feasible P-Q aggregation domain, and calculating the minimum running cost of the micro-grid corresponding to each P-Q sampling point based on the double-port micro-grid model to construct an aggregation cost curved surface; embedding the feasible P-Q aggregation domain and the aggregation cost curved surface into a rolling scheduling model of a superior grid at the same time, and solving the scheduling instruction of the micro-grid at two ports in the current period; scheduling the micro-grid according to the scheduling instruction to obtain the distributed energy running state in the next period, and repeating the above steps in the next scheduling period.

Description

Technical Field

[0001] This application relates to the field of electrical engineering technology, and more specifically, to a two-port microgrid PQ aggregation domain and a rolling scheduling method for aggregation cost surfaces. Background Technology

[0002] Microgrids, as adjustable units integrating a large number of distributed energy sources, are receiving increasing attention. The distributed energy generation and consumption characteristics in microgrids are flexible, highly volatile, and intermittent. Furthermore, distributed energy sources exhibit large total power but small individual power, placing significant computational burden on the communication and management of power systems.

[0003] Aggregating microgrids allows their complex internal models to be represented by low-dimensional aggregation results, effectively reducing the number of constraints and variables that the upper-level power grid needs to consider during dispatching and operation. This reduces the complexity of power system operation and improves the management efficiency of distributed energy resources.

[0004] However, existing microgrid aggregation technologies still have the following shortcomings in practical applications: First, microgrid aggregation needs to fully consider the operating characteristics of different types of distributed energy sources and network constraints to ensure that all dispatch commands within the aggregation interval can be deaggregated into feasible operating schemes within the microgrid. Current technologies are not yet perfect in this regard, making it difficult to guarantee the complete feasibility of the aggregation results.

[0005] Second, with the development of microgrid technology, more and more microgrids are adopting a two-port access method, that is, the microgrid is connected to the upper-level grid through two ports. In this case, there is a mutual coupling relationship between the power exchange between the two ports, and most existing aggregation methods are designed for single-port microgrids, which are difficult to effectively handle the power coupling characteristics between ports in two-port microgrids.

[0006] Third, the operational economy of microgrids is a crucial factor that must be considered when participating in the dispatching of the upper-level power grid. How to effectively retain the cost information of microgrids at the port in the aggregation results, so that the upper-level power grid can take into account the operational economy of microgrids when making dispatching decisions, is also a current technological challenge.

[0007] In summary, there is an urgent need for a scheduling method that can simultaneously address the feasibility of two-port microgrid aggregation, the characterization of power coupling between ports, and the retention of operating cost information. Summary of the Invention

[0008] To address at least one defect or improvement requirement in the prior art, this invention provides a rolling scheduling method for a PQ aggregation domain and aggregation cost surface in a two-port microgrid, which solves the technical problems of low aggregation feasibility, difficulty in characterizing the power coupling relationship between ports, and difficulty in retaining operating cost information in a two-port microgrid.

[0009] To achieve the above objectives, according to a first aspect of the present invention, a rolling scheduling method for a two-port microgrid PQ aggregation domain and aggregation cost surface is provided. The method includes: establishing a two-port microgrid model based on the operating state of distributed energy resources in the current scheduling period; the two-port microgrid model includes inequality constraints describing the operating constraints of each distributed energy resource within the microgrid, and equality constraints describing the power balance and network flow constraints within the microgrid; constructing an initial PQ aggregation domain for the microgrid, and based on the two-port microgrid model, cutting the initial PQ aggregation domain through iterative solution until a convergent feasible PQ aggregation domain is obtained; the feasible PQ aggregation domain is used to characterize the... This involves identifying all feasible combinations of active and reactive power interactions between the two ports of the microgrid and the upper-level grid, as well as the coupling constraints between the interactive power at the two ports. Multiple PQ sampling points are selected within the feasible PQ aggregation domain, and the minimum operating cost of the microgrid corresponding to each PQ sampling point is calculated based on the dual-port microgrid model to construct an aggregation cost surface. The feasible PQ aggregation domain and the aggregation cost surface are simultaneously embedded into the rolling dispatch model of the upper-level grid to obtain the dispatch instructions for the microgrid at the two ports during the current time period. Internal dispatching of the microgrid is performed according to the dispatch instructions to obtain the distributed energy operating status for the next time period, and the above steps are repeated in the next dispatch period.

[0010] In an exemplary embodiment, the step of cutting the initial PQ aggregation domain based on the two-port microgrid model through iterative solution includes: modeling the cutting plane used to cut the initial PQ aggregation domain as a max-min bilevel optimization problem; in the m-th iteration, based on the two-port microgrid model, solving the cutting plane under the current PQ aggregation domain, and using the cutting plane to cut the current PQ aggregation domain to obtain the updated PQ aggregation domain; repeating the above iterative steps until a preset convergence condition is met to obtain the feasible PQ aggregation domain.

[0011] In an exemplary embodiment, in the max-min bi-level optimization problem, the outer optimization is used to determine the target scheduling command that requires the microgrid to meet the operational constraints at the maximum corrected power; the inner optimization is used to minimize the required corrected power given the target scheduling command; the convergence condition is that the minimum value of the corrected power is equal to zero; and solving the cutting plane under the current PQ aggregation domain includes: performing a dual transformation on the inner optimization problem in the max-min bi-level optimization problem and processing it using the KKT conditions to obtain the cutting plane.

[0012] In an exemplary embodiment, constructing the aggregation cost surface includes: within the feasible PQ aggregation domain, performing grid-based sampling of the interactive power of one of the ports to obtain multiple PQ sampling points; for each PQ sampling point, solving the internal economic dispatch optimization problem of the microgrid based on the two-port microgrid model to obtain the corresponding minimum operating cost; constructing a three-dimensional point set based on all the PQ sampling points and their corresponding minimum operating costs, and generating the aggregation cost surface composed of multiple piecewise linear planes based on the convex hull lower envelope surface of the three-dimensional point set.

[0013] In an exemplary embodiment, the aggregate cost surface includes a first aggregate cost surface and a second aggregate cost surface, corresponding to the first port and the second port of the microgrid, respectively; each aggregate cost surface is composed of multiple piecewise linear planes, and the operating cost of the microgrid is greater than or equal to the value corresponding to each piecewise linear plane, wherein the value corresponding to each piecewise linear plane is determined by a linear combination of the constant coefficient of the piecewise linear plane and the interactive active power and interactive reactive power of the corresponding port.

[0014] In an exemplary embodiment, embedding the feasible PQ aggregation domain and the aggregation cost surface simultaneously into the rolling dispatch model of the upper-level power grid includes: using the feasible PQ aggregation domain as a feasible domain constraint of the rolling dispatch model of the upper-level power grid; using the aggregation cost surface as a cost constraint of the rolling dispatch model of the upper-level power grid; and the rolling dispatch model of the upper-level power grid aims to minimize the sum of the operating cost of the upper-level power grid itself and the operating cost of the microgrid.

[0015] In an exemplary embodiment, the step of internally scheduling the microgrid according to the scheduling instruction includes: using the scheduling instruction as a boundary condition and minimizing the internal operating cost of the microgrid as the objective, solving the problem based on the two-port microgrid model to obtain the output plan of each distributed energy source within the microgrid, and updating the operating status of the distributed energy sources.

[0016] In one exemplary embodiment, the distributed energy source includes at least a micro gas turbine, an energy storage unit, and distributed photovoltaics; the operational constraints include at least the upper and lower limits of the output of the micro gas turbine and ramping constraints, the charging and discharging power and capacity constraints of the energy storage unit, and the output and curtailment constraints of the distributed photovoltaics.

[0017] According to a second aspect of the invention, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, wherein the computer program is configured to execute the above-described two-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method at runtime.

[0018] According to a third aspect of the present invention, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method through the computer program.

[0019] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: (1) This invention provides a rolling scheduling method for a PQ aggregation domain and aggregation cost surface of a two-port microgrid. It involves gridded sampling within the feasible PQ aggregation domain and calculating the minimum operating cost of the microgrid corresponding to each sampling point, thereby constructing an aggregation cost surface composed of multiple piecewise linear planes. This aggregation cost surface accurately characterizes the operating cost of the microgrid under different port interaction power levels, enabling the upper-level grid to incorporate the microgrid's operational economy into its optimization objective when making scheduling decisions, achieving globally optimal economic scheduling. The constructed feasible PQ aggregation domain is not a simple combination of the independent operating ranges of the two ports, but rather a precise polyhedral region obtained through iterative cutting. It can simultaneously characterize all feasible combinations of active and reactive power interactions between the two ports and the upper-level grid, as well as the coupling constraint relationship between the interaction power of the two ports. It is particularly suitable for two-port microgrid scenarios, overcoming the shortcomings of existing aggregation methods, which are mostly designed for single-port microgrids.

[0020] (2) This invention adopts a rolling scheduling method. For each scheduling period, a two-port microgrid model is re-established based on the current operating status of distributed energy resources (including the output of micro gas turbines and the energy storage status), and the feasible PQ aggregation domain and aggregation cost surface are re-solved. This allows the aggregation boundary and aggregation cost to be updated in real time with the dynamic changes in the operating status of distributed energy resources, improving the adaptability and accuracy of scheduling, and avoiding the scheduling deviation problem caused by the static aggregation method being detached from the actual operating status.

[0021] (3) This invention aggregates the complex internal model of the microgrid into a low-dimensional feasible PQ aggregation domain and aggregation cost surface. The upper-level power grid only needs to consider a few constraints to make decisions during scheduling, thereby reducing the complexity of operation and enabling the upper-level power grid to consider the economic efficiency of microgrid operation at the same time. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic flowchart illustrating an optional dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method provided in this application embodiment; Figure 2 A conceptual schematic diagram illustrating the interactive power operation of an optional dual-port microgrid with an upstream power grid, provided for an embodiment of this application; Figure 3 A topology diagram of an optional distribution network based on a 33-node system and a dual-port microgrid based on a 28-node system provided for embodiments of this application; Figure 4 A schematic diagram of the daily distributed photovoltaic output of an optional dual-port microgrid provided in this application embodiment; Figure 5(a) is a schematic diagram of the total load of an optional distribution network provided in an embodiment of this application; Figure 5(b) is a schematic diagram of the total load of an optional microgrid provided in an embodiment of this application; Figure 6(a) is a schematic diagram of the aggregate cost surface of port 1 for each time period obtained by an optional rolling scheduling according to an embodiment of this application; Figure 6(b) is a schematic diagram of the aggregate cost surface of port 2 for each time period obtained by an optional rolling scheduling according to an embodiment of this application; Figure 7(a) is a schematic diagram of the interactive power of port 1 in different time periods obtained by an optional rolling scheduling according to an embodiment of this application; Figure 7(b) is a schematic diagram of the interactive power of port 2 in different time periods obtained by an optional rolling scheduling according to an embodiment of this application; Figure 8(a) is a schematic diagram of the output of a micro gas turbine in a dual-port microgrid obtained by an optional rolling scheduling according to an embodiment of this application; Figure 8(b) is a schematic diagram of the output of a dual-port microgrid energy storage unit obtained by an optional rolling scheduling according to an embodiment of this application; Figure 8(c) is a schematic diagram of the output of a dual-port microgrid distributed photovoltaic system obtained by an optional rolling scheduling according to an embodiment of this application. Figure 8(d) is a schematic diagram of the output of a dual-port microgrid reactive power compensation device obtained by optional rolling scheduling according to an embodiment of this application; Figure 9 This is a schematic diagram of an optional electronic device provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0025] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0026] According to one aspect of the embodiments of this application, a dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method are provided. The following is in conjunction with... Figure 1 This application describes the dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method provided in the embodiments of this application.

[0027] Figure 1 This is a flowchart illustrating an optional dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method provided in an embodiment of this application, as shown below. Figure 1 As shown, the process of this method may include the following steps: S102, Based on the operating status of distributed energy resources in the microgrid during the current scheduling period, a two-port microgrid model is established; the two-port microgrid model includes inequality constraints describing the operating constraints of each distributed energy resource within the microgrid, and equality constraints describing the power balance and network flow constraints within the microgrid. S104, Construct the initial PQ aggregation domain of the microgrid, and based on the two-port microgrid model, cut the initial PQ aggregation domain through iterative solution until a convergent feasible PQ aggregation domain is obtained; the feasible PQ aggregation domain is used to characterize all feasible combinations of active and reactive power interaction between the two ports of the microgrid and the upper-level grid, as well as the coupling constraints between the power interaction between the two ports. S106, select multiple PQ sampling points within the feasible PQ aggregation domain, and calculate the minimum operating cost of the microgrid corresponding to each PQ sampling point based on the dual-port microgrid model, so as to construct an aggregation cost surface; S108, The feasible PQ aggregation domain and the aggregation cost surface are simultaneously embedded into the rolling scheduling model of the upper-level power grid to solve for the scheduling instructions of the microgrid at the two ports in the current time period. S110, perform internal scheduling of the microgrid according to the scheduling instruction to obtain the operating status of distributed energy in the next time period, and repeat the above steps in the next scheduling period.

[0028] The rolling scheduling method for the PQ aggregation domain and aggregation cost surface of a dual-port microgrid provided in this application embodiment is applicable to the technical scenario where a dual-port microgrid is connected to the distribution network and participates in the daily scheduling and operation of the distribution network.

[0029] Optionally, combined Figure 1 and Figure 2 As shown, distributed energy includes at least micro gas turbines, energy storage units, and distributed photovoltaics; inequality constraints include at least the upper and lower limits of output and ramping constraints of micro gas turbines, the charging and discharging power constraints and capacity constraints of energy storage units, and the output constraints and curtailment rate constraints of distributed photovoltaics; equality constraints include at least node power balance constraints and node voltage constraints.

[0030] For example, firstly, the operating status of the distributed energy resources of the microgrid during the current scheduling period is obtained. This operating status includes at least information such as the current output value of the micro gas turbine and the current energy storage value of the energy storage unit. Then, based on this operating status, a two-port microgrid model describing the internal operating characteristics of the microgrid is established. The microgrid has two ports connected to the upper-level grid (e.g., port 1 and port 2). Both ports can exchange active and reactive power with the upper-level grid, and there is a correlation between the power that the two ports can exchange with the upper-level grid.

[0031] Furthermore, an initial PQ aggregation domain for the microgrid is constructed. Optionally, the initial PQ aggregation domain is a rectangular region defined by the independent upper and lower limits of the interaction power of the two ports, and the range of this rectangular region can be obtained by solving an optimization problem.

[0032] Then, based on the established two-port microgrid model, the initial PQ aggregation domain is segmented through iterative solution, gradually removing infeasible spaces until a convergent feasible PQ aggregation domain is obtained. The feasible PQ aggregation domain is used to characterize all feasible combinations of active and reactive power interactions between the two ports of the microgrid and the upper-level grid, as well as the coupling constraints between the power interactions between the two ports.

[0033] Optionally, the initial PQ aggregation domain is cut through iterative solution, including: modeling the cutting plane used to cut the initial PQ aggregation domain as a max-min bilevel optimization problem; in the m-th iteration, based on the two-port microgrid model, solving the cutting plane under the current PQ aggregation domain, and using the cutting plane to cut the current PQ aggregation domain to obtain the updated PQ aggregation domain; repeating the above iterative steps until the preset convergence condition is met to obtain a feasible PQ aggregation domain.

[0034] Optionally, in the max-min two-level optimization problem, the outer optimization is used to determine a target scheduling command that requires the microgrid to have the maximum corrected power to meet the operating constraints; the inner optimization is used to minimize the required corrected power given the target scheduling command; the convergence condition is that the minimum value of the corrected power is equal to zero.

[0035] Optionally, solving for the cutting plane under the current PQ aggregation domain includes: performing a dual transformation on the inner optimization problem in the max-min bi-level optimization problem, and processing it using the KKT (Karush-Kuhn-Tucker) conditions to obtain the cutting plane.

[0036] Within the obtained feasible PQ aggregation domain, the interactive power of one of the ports is sampled in a grid to obtain multiple PQ sampling points. Optionally, the sampling method is as follows: the active power range is divided into M segments at equal intervals, and for each active power sampling point, the upper and lower bounds of reactive power under that active power are solved. Then, the reactive power range is divided into N segments at equal intervals to obtain M×N PQ sampling points.

[0037] Then, for each PQ sampling point, based on the established two-port microgrid model, the internal economic dispatch optimization problem of the microgrid is solved to obtain the corresponding minimum operating cost.

[0038] Finally, based on all PQ sampling points and their corresponding minimum operating costs, a three-dimensional point set is constructed, and based on the convex hull lower envelope of the three-dimensional point set, an aggregate cost surface composed of multiple piecewise linear planes is generated.

[0039] Optionally, the aggregate cost surface includes a first aggregate cost surface and a second aggregate cost surface, corresponding to the first port and the second port of the microgrid, respectively; each aggregate cost surface is composed of multiple piecewise linear planes, and the operating cost of the microgrid is greater than or equal to the value corresponding to each piecewise linear plane. The value corresponding to each piecewise linear plane is determined by a linear combination of the constant coefficient of the piecewise linear plane and the interactive active power and interactive reactive power of the corresponding port.

[0040] The obtained feasible PQ aggregation domain and the constructed aggregation cost surface are simultaneously embedded into the rolling dispatch model of the upper-level power grid. The feasible PQ aggregation domain serves as the feasible domain constraint of the upper-level power grid's rolling dispatch model, ensuring the feasibility of dispatch commands; the aggregation cost surface serves as the cost constraint of the upper-level power grid's rolling dispatch model, reflecting the operational economy of the microgrid.

[0041] The rolling dispatch model of the upper-level power grid aims to minimize the sum of the operating costs of the upper-level power grid and the operating costs of the microgrid, and solves for the dispatch instructions of the microgrid at the two ports in the current period.

[0042] Finally, based on the obtained scheduling instructions, the microgrid is internally scheduled. Specifically, using the scheduling instructions as boundary conditions and minimizing the internal operating cost of the microgrid as the objective, a two-port microgrid model is used to obtain the output plan of each distributed energy source within the microgrid, and the operating status of the distributed energy sources, including the output of the micro gas turbine and the energy status of the energy storage unit, is updated.

[0043] Then, increment the current time period by one, enter the next scheduling time period, and repeat steps S102 to S110 to achieve rolling scheduling.

[0044] Through steps S102 to S110, a two-port microgrid model is established based on the distributed energy operation status of the microgrid during the current scheduling period. An initial PQ aggregation domain of the microgrid is constructed, and based on the two-port microgrid model, the initial PQ aggregation domain is iteratively divided until a convergent feasible PQ aggregation domain is obtained. Multiple PQ sampling points are selected within the feasible PQ aggregation domain, and the minimum operating cost of the microgrid corresponding to each PQ sampling point is calculated based on the two-port microgrid model to construct an aggregation cost surface. The feasible PQ aggregation domain and the aggregation cost surface are simultaneously embedded into the rolling scheduling model of the upper-level power grid to obtain the scheduling instructions for the microgrid at both ports during the current period. Internal scheduling of the microgrid is performed according to the scheduling instructions to obtain the distributed energy operation status for the next period, and the above steps are repeated in the next scheduling period. This solves the technical problems of low aggregation feasibility of two-port microgrids, difficulty in representing the power coupling relationship between ports, and difficulty in retaining operating cost information, thus improving the adaptability, accuracy, and economy of scheduling.

[0045] In one optional example, the components of the power grid are micro gas turbines, energy storage units, and distributed photovoltaics; at time t... For nodes, This represents the active power output of the micro gas turbine. This represents the reactive power output of the micro gas turbine. This represents the active power output of the energy storage unit. This represents the reactive power output of the energy storage unit. This represents the stored energy value. This refers to the active power output of distributed photovoltaic systems. This refers to the reactive power output of distributed photovoltaic systems. The active power flowing through the transmission line between nodes. for Reactive power flowing on the transmission lines between nodes for The square of the node voltage, for The square of the node voltage; the operational constraints and network constraints of distributed energy resources in the microgrid are as follows: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) Equations (1)-(3) represent the operational constraints of the micro gas turbine; Equations (4)-(7) represent the operational constraints of the energy storage unit; Equations (8)-(9) represent the output constraints of the distributed photovoltaic system; Equations (10)-(15) represent the network constraints; specifically, This represents the upper limit of the power output of a micro gas turbine. This represents the lower limit of the power output of a micro gas turbine. To push the power output of micro gas turbines to the upper limit, The upper limit of the power output of micro gas turbines; This represents the upper limit of the power generation capacity of the energy storage unit. This represents the upper limit of the energy storage capacity of the energy storage unit. This is the upper limit of energy storage. As the lower limit of 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 load, This is the reactive load. This represents the upper limit of the active power of the transmission line. This is the lower limit of the active power of the transmission line. This is the upper limit of the reactive power of the transmission line. This is the lower limit of the reactive power of the transmission line. This is the upper limit of the square of the node voltage. This is the lower limit of the square of the node voltage. The resistance of the transmission line, The inductance of the transmission line; These are the phase angles of the micro gas turbine. The phase angle of the energy storage unit. The phase angle for distributed photovoltaic power; The initial state of the distributed energy source. This represents the active power output of the micro gas turbine in its initial state. This represents the initial energy storage value.

[0046] Preferably, the model of the power grid and the constraints of its aggregate operating range can be written in the following compact form: (16) (17) (18) (19) in, Let be a vector consisting of all decision variables. The magnitude of the power exchanged between the two ports and the upstream power grid, where, Let be the active power of the interaction between port 1 and the upstream power grid during the t-th time period. Let be the reactive power exchanged between port 1 and the upstream power grid during the t-th time period. Let be the active power of the interaction between port 2 and the upstream power grid during the t-th time period. Let be the reactive power of the interaction between port 2 and the upstream power grid during the t-th time period; Equation (16) corresponds to all inequality constraints, and Equation (17) corresponds to all equality constraints; parameter matrix sum coefficient vector All are determined by the various constraints; The initial values ​​for the PQ aggregation domain of the two-port microgrid are given. The coefficient matrix, It is a 4×4 identity matrix. For parameter vectors; This represents the upper limit of active power for interaction between port 1 and the upstream power grid. This is the upper limit of reactive power exchanged between port 1 and the upstream power grid. This represents the upper limit of active power for interaction between port 2 and the upstream power grid. This represents the upper limit of reactive power for interaction between port 2 and the upstream power grid. This is the lower limit of the active power for interaction between port 1 and the upstream power grid. The lower limit of reactive power interaction between port 1 and the upstream power grid. This is the lower limit of the active power for interaction between port 2 and the upstream power grid. The lower limit of reactive power for interaction between port 2 and the upstream power grid can be obtained from the following optimization problem: (20) (twenty one) in, This refers to the active power exchanged between port 1 and the upstream power grid. for The relationship matrix between the active power of the interaction with port 1 and the upper and lower limits of the other interaction power can be obtained similarly to equations (20)-(21).

[0047] Understandably, due to the initial PQ aggregation domain There are infeasible spaces, so a cutting plane is required to cut away these infeasible spaces. In this iteration, the solution for the cutting plane is modeled as the following max-min problem: (twenty two) (twenty three) Equation (22) is a max-min problem. This is the vector corresponding to the upward power correction value. This is the vector corresponding to the downward power correction value, due to the PQ aggregation domain in the current iteration. Dispatch instructions issued There may be parts that are not feasible, so a power correction value is used. To make it feasible, when the solution result of problem (22) is 0, it means that no matter what scheduling instruction is made, no power correction value is needed to make it feasible, which means that it is iterated to the feasible PQ aggregation domain; It is a column vector consisting entirely of 1s. and Due to grid constraints, For the current number The PQ aggregation domain in the next iteration. For the first The coefficient matrix in the next iteration For the first The parameter vector in the next iteration. This is used to limit the two power correction values ​​to positive values.

[0048] By performing a dual transformation on the inner min problem in equation (22), we obtain the unified max dual problem as follows: (twenty four) (25) in, As dual variables, Since it is a bilinear term, it is equivalently treated using the KKT conditions as follows: (26) (27) in, For KKT multipliers, Let the variable be a Boolean variable; solving problems (26)-(27) will yield the solution values ​​of the dual variable. .

[0049] For equation (24), considering that the original max-min problem, when solved to the feasible PQ aggregation domain, During the iteration process, a power correction value is required. Therefore there is Considering strong duality, the cut plane corresponding to the infeasible space can be removed. The situation; therefore, the first The cutting plane within the PQ aggregation domain obtained in the next iteration The following is the result: (28) Rewrite (28) in the following form: (29) (30) In other words, the The corresponding generation will occur in the next iteration. As a cutting plane This is the coefficient matrix in this iteration. Let be the parameter vector in this iteration. This is equivalent to denoting the cutting plane generated in each iteration during the PQ aggregation domain solution over time interval t. .

[0050] Iterate over the PQ aggregation domain cut by the projective cutting plane. as follows: (31) make Repeat the above steps until The PQ aggregation domain obtained at this point is a feasible PQ aggregation domain for a two-port microgrid. , The coefficient matrix, For parameter vectors.

[0051] In the PQ aggregation domain of dual-port microgrids Within the port, a total of M×N PQ sampling points are selected for port 1 as follows: (32) (33) (34) (35) (36) (37) Where M is the number of power point segments during sampling, and N is the number of reactive power point segments during sampling; For the first The active power interacting between port 1 of each power point segment and the upstream power grid. For the first The reactive power interacting between port 1 of each power point segment and the upstream power grid. This refers to the reactive power exchanged between port 1 and the upstream power grid. The active power for interaction with port 1 is The upper limit of reactive power of port 1 at that time. The active power for interaction with port 1 is The lower bound of the reactive power of port 1 at that time can be obtained by solving (34)-(35); To select the interval of the active power point, To select the interval for reactive power points; let the selected PQ sampling points be denoted as... Based on each PQ sampling point, the optimal cost of the microgrid at that sampling point is calculated as follows: (38) (39) Equation (38) is the solution for the minimum cost. The min problem for The cost coefficient of a node-based micro gas turbine, for The cost coefficient of the energy storage unit at the node. for The curtailment penalty coefficient of distributed photovoltaic power at the node; Equation (39) is the constraint of the optimization problem; traversing all sampling points Construct a three-dimensional point set based on all sampling points and their cost values. .

[0052] A point set G can correspond to a three-dimensional convex hull composed of several triangular facets; for any triangular facet, let the facet be s, and let its three vertices be... Va , Vb , Vc as follows: (40) With vertex Va For example, in the constructed PQC three-dimensional space, To form vertices Va The three coordinate values ​​are the same for the other vertices.

[0053] The outer normal vector of this triangular facet is calculated as follows: (41) in, Let x, y, and z be the components of the outward normal vector of the triangular facet.

[0054] Therefore, the equation of the plane containing the triangular facet can be obtained. : (42) (43) in, The active power parameter is the equation of the plane containing the triangular facet. The reactive power parameter is the equation of the plane containing the triangular facet. is a constant coefficient of the equation of the plane containing the triangular facet, P is the active power, and Q is the reactive power.

[0055] All points in point set G Substitute all If all points in point set G satisfy equation (44), then the triangular facet s is the lower envelope plane of the cost point set: (44) Repeat the above calculation and filtering process for all triangular facets s to obtain a piecewise linear lower envelope plane composed of several lower envelope planes, as follows: (45) Where S represents the total number of lower envelope planes, and all lower envelope planes together constitute the interaction power with respect to port 1. The aggregation cost surface represents the aggregation cost surface when the port 1 interaction power is... At that time, the operating cost of microgrids Greater than or equal to the values ​​corresponding to all lower envelope planes The aggregation cost surface and operating cost of port 2 Similarly, we can obtain the following.

[0056] Will , and Simultaneously, it is embedded in the upper-level power grid rolling dispatch model as follows: (46) (47) in, A vector consisting of decision variables from the upper-level power grid. and These are the equality constraints and inequality constraints of the upper-level power grid, respectively. It is the cost function of the upper-level power grid itself; The running cost function obtained by integrating all lower envelope planes for the port 1 aggregate cost surface. coefficient, The running cost function obtained by integrating all lower envelope planes for the port 2 aggregate cost surface. The coefficient.

[0057] Solving (46)-(47) yields the scheduling instructions at the two-port of the microgrid. Based on The microgrid is dispatched as follows: (48) (49) here, This represents the cost coefficient for micro gas turbines. The cost coefficient of the energy storage unit. Let be the curtailment penalty coefficient for distributed photovoltaic power. Scheduling can obtain the distributed energy state at time t+1, let And the above scheduling process is rolled out continuously.

[0058] In an optional example, the rolling scheduling method based on the PQ aggregation domain and aggregation cost surface of a two-port microgrid includes the following steps: at the t-th time period, the two-port microgrid model is obtained based on the distributed energy state modeling of the current time period. , ; Let the initial value of the two-port PQ aggregation field be... The solution to the PQ aggregation domain is modeled as a max-min bilevel optimization problem; in the first... In the next iteration, the solution is found in the current... The cutting plane below This is used to iterate the two-port PQ aggregation domain. Repeat the above steps until convergence; in the obtained PQ aggregation domain Select several PQ sampling points And calculate the corresponding minimum operating cost of the microgrid for these sampling points. Construct a point set based on the sampling points and their cost values. The aggregation cost surface and operating cost of port 1 and port 2 are generated from this point set. and ;Will , and Simultaneously, it is embedded into the rolling dispatch model of the upper-level power grid to obtain the dispatch instructions at the dual ports of the microgrid. Based on The microgrid is scheduled to obtain the distributed energy status in time period t+1, and the above process is repeated in rolling during time period t+1.

[0059] The following description, using a specific application scenario, further illustrates the beneficial effects achievable in this embodiment. Tests were conducted on a 28-node dual-port microgrid and a 33-node distribution network. The dual-port microgrid includes two micro gas turbines, two energy storage units, two reactive power compensation devices, and eight distributed photovoltaic units. The distribution network includes four diesel engines and two reactive power compensation devices. The active power range at the ports where the distribution network interacts with the transmission network is [range missing]. The reactive power range is .like Figure 3 The diagram shows the topology of a distribution network based on a 33-node system and a two-port microgrid based on a 28-node system. The daily distributed photovoltaic output in the microgrid is as follows: Figure 4As shown in Figure 5(a), the total load of the distribution network is illustrated; as shown in Figure 5(b), the total load of the microgrid is illustrated. The parameters of the micro gas turbines in the microgrid are shown in Table 1; the parameters of the energy storage units in the microgrid are shown in Table 2; the parameters of the distributed photovoltaic systems in the microgrid are shown in Table 3; and the data for the reactive power compensation devices in the microgrid are shown in Table 4. The parameters of the diesel generators in the distribution network are shown in Table 5; and the parameters of the reactive power compensation devices in the distribution network are shown in Table 6. Details are as follows: Table 1 Parameters of micro gas turbines in microgrids

[0060] Table 2 Parameters of Energy Storage Units in Microgrids

[0061] Table 3 Distributed Photovoltaic Parameters for Microgrids

[0062] Table 4 Parameters of Microgrid Reactive Power Compensation Device

[0063] Table 5 Parameters of Diesel Engines in Power Distribution Network

[0064] Table 6 Parameters of Reactive Power Compensation Devices in Distribution Networks

[0065] For example, a corresponding program was written in the computational software MATLAB R2024a to perform the simulation.

[0066] 1,000 dispatch instructions are generated in the PQ aggregation domain of the microgrid for each time period, totaling 24,000 dispatch instructions for 24 time periods. Within each time period, the system tests whether the microgrid can operate normally and inputs all dispatch instructions into the power grid to test whether the power grid can operate normally.

[0067] Table 7 Dispatch Results of Power Grid Operation Section

[0068] The results in Table 7 show that all 10,000 scheduling instructions can be successfully executed by the power grid, proving the feasibility of the optional dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method provided in this application. The aggregation cost surface of port 1 for each time period obtained by rolling scheduling is shown in Figure 6(a), and the aggregation cost surface of port 2 for each time period obtained by rolling scheduling is shown in Figure 6(b); Figure 7(a) shows the interactive power of port 1 for each time period obtained by rolling scheduling, and Figure 7(b) shows the interactive power of port 2 for each time period obtained by rolling scheduling; Figure 8(a) shows the output of the micro gas turbine in the dual-port microgrid obtained by rolling scheduling, Figure 8(b) shows the output of the energy storage unit in the dual-port microgrid obtained by rolling scheduling, Figure 8(c) shows the output of the distributed photovoltaic in the dual-port microgrid obtained by rolling scheduling, and Figure 8(d) shows the output of the reactive power compensation device in the dual-port microgrid obtained by rolling scheduling. Simultaneously, the scheduling time obtained by utilizing the PQ aggregation domain and aggregation cost surface of the dual-port microgrid in the power grid rolling scheduling is recorded as follows: Table 8 Dispatch Results of Power Grid Operation Section

[0069] As shown in Table 8, using the PQ aggregation domain and aggregation cost surface for scheduling can significantly reduce the scheduling time, ensuring that the distribution network scheduling time and microgrid de-aggregation time do not exceed 0.2s and the microgrid aggregation time does not exceed 40s. The solution is efficient and suitable for rolling scheduling.

[0070] In summary, simulation results demonstrate that this invention can reduce the complexity of the upper-level power grid dispatching model and improve the feasibility and economy of two-port microgrids participating in rolling dispatch while preserving the feasible boundary and economic information of two-port PQ.

[0071] According to another aspect of the embodiments of this application, a storage medium is also provided. Optionally, in this embodiment, the storage medium can be used to execute the program code of any of the dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling methods described in the embodiments of this application.

[0072] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: S1. Based on the operating status of distributed energy resources in the microgrid during the current scheduling period, a two-port microgrid model is established. The two-port microgrid model includes inequality constraints describing the operating constraints of each distributed energy resource within the microgrid, and equality constraints describing the power balance and network flow constraints within the microgrid. S2, Construct the initial PQ aggregation domain of the microgrid, and based on the two-port microgrid model, cut the initial PQ aggregation domain through iterative solution until a convergent feasible PQ aggregation domain is obtained; the feasible PQ aggregation domain is used to characterize all feasible combinations of active and reactive power interaction between the two ports of the microgrid and the upper-level grid, as well as the coupling constraints between the power interaction between the two ports. S3, select multiple PQ sampling points within the feasible PQ aggregation domain, and calculate the minimum operating cost of the microgrid corresponding to each PQ sampling point based on the dual-port microgrid model, so as to construct the aggregation cost surface; S4, simultaneously embed the feasible PQ aggregation domain and the aggregation cost surface into the rolling scheduling model of the upper-level power grid, and solve to obtain the scheduling instructions of the microgrid at the two ports in the current time period; S5. Perform internal scheduling of the microgrid according to the scheduling instructions to obtain the operating status of distributed energy in the next time period, and repeat the above steps in the next scheduling period.

[0073] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated in this embodiment.

[0074] The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0075] According to another aspect of the embodiments of this application, an electronic device is also provided for implementing the above-described dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method. The electronic device may be a server, a terminal, or a combination thereof.

[0076] Figure 9 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application, such as... Figure 9 As shown, it includes a processor 902, a communication interface 904, a memory 906, and a communication bus 908. The processor 902, communication interface 904, and memory 906 communicate with each other via the communication bus 908. Memory 906 is used to store computer programs; When processor 902 executes a computer program stored in memory 906, it performs the following steps: S1. Based on the operating status of distributed energy resources in the microgrid during the current scheduling period, a two-port microgrid model is established. The two-port microgrid model includes inequality constraints describing the operating constraints of each distributed energy resource within the microgrid, and equality constraints describing the power balance and network flow constraints within the microgrid. S2, Construct the initial PQ aggregation domain of the microgrid, and based on the two-port microgrid model, cut the initial PQ aggregation domain through iterative solution until a convergent feasible PQ aggregation domain is obtained; the feasible PQ aggregation domain is used to characterize all feasible combinations of active and reactive power interaction between the two ports of the microgrid and the upper-level grid, as well as the coupling constraints between the power interaction between the two ports. S3, select multiple PQ sampling points within the feasible PQ aggregation domain, and calculate the minimum operating cost of the microgrid corresponding to each PQ sampling point based on the dual-port microgrid model, so as to construct the aggregation cost surface; S4, simultaneously embed the feasible PQ aggregation domain and the aggregation cost surface into the rolling scheduling model of the upper-level power grid, and solve to obtain the scheduling instructions of the microgrid at the two ports in the current time period; S5. Perform internal scheduling of the microgrid according to the scheduling instructions to obtain the operating status of distributed energy in the next time period, and repeat the above steps in the next scheduling period.

[0077] Optionally, the communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 The symbol is represented by a single thick line, but this does not indicate that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned electronic device and other devices.

[0078] Memory may include RAM or non-volatile memory. Volatile memory, for example, at least one disk storage device. Alternatively, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0079] The processors mentioned above can be general-purpose processors, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; they can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0080] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0081] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0082] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0083] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0084] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A rolling scheduling method for the PQ aggregation domain and aggregation cost surface of a dual-port microgrid, characterized in that, include: Based on the operating status of distributed energy resources in the microgrid during the current scheduling period, a two-port microgrid model is established. The two-port microgrid model includes inequality constraints describing the operating constraints of each distributed energy resource within the microgrid, and equality constraints describing the power balance and network flow constraints within the microgrid. An initial PQ aggregation domain is constructed for the microgrid, and based on the two-port microgrid model, the initial PQ aggregation domain is cut through iterative solution until a convergent feasible PQ aggregation domain is obtained. The feasible PQ aggregation domain is used to characterize all feasible combinations of active and reactive power interaction between the two ports of the microgrid and the upper-level grid, as well as the coupling constraints between the power interaction between the two ports. Multiple PQ sampling points are selected within the feasible PQ aggregation domain, and the minimum operating cost of the microgrid corresponding to each PQ sampling point is calculated based on the dual-port microgrid model to construct an aggregation cost surface. The feasible PQ aggregation domain and the aggregation cost surface are simultaneously embedded into the rolling scheduling model of the upper-level power grid to obtain the scheduling instructions of the microgrid at the two ports in the current time period. The microgrid is internally scheduled according to the scheduling instructions to obtain the operating status of distributed energy resources in the next time period, and the above steps are repeated in the next scheduling period.

2. The dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method as described in claim 1, characterized in that, The step of dividing the initial PQ aggregation domain based on the dual-port microgrid model through iterative solution includes: The solution for the cutting plane used to cut the initial PQ aggregation domain is modeled as a max-min bilevel optimization problem. In the m-th iteration, based on the dual-port microgrid model, the cutting plane under the current PQ aggregation domain is solved, and the cutting plane is used to cut the current PQ aggregation domain to obtain the updated PQ aggregation domain. Repeat the above iterative steps until the preset convergence condition is met to obtain the feasible PQ aggregation domain.

3. The dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method as described in claim 2, characterized in that, In the max-min two-level optimization problem, the outer optimization is used to determine the target scheduling command that requires the microgrid to achieve the maximum corrected power to meet the operating constraints; the inner optimization is used to minimize the required corrected power given the target scheduling command. The convergence condition is that the minimum value of the corrected power is equal to zero, and the solution for the cutting plane in the current PQ aggregation domain includes: The inner optimization problem in the max-min bilayer optimization problem is subjected to dual transformation, and the KKT conditions are used to solve for the cutting plane.

4. The dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method as described in claim 1, characterized in that, The construction of the aggregation cost surface includes: Within the feasible PQ aggregation domain, the interaction power of one of the ports is sampled in a grid pattern to obtain multiple PQ sampling points; For each PQ sampling point, based on the dual-port microgrid model, the internal economic dispatch optimization problem of the microgrid is solved to obtain the corresponding minimum operating cost; Based on all the PQ sampling points and their corresponding minimum operating costs, a three-dimensional point set is constructed, and based on the convex hull lower envelope of the three-dimensional point set, the aggregate cost surface composed of multiple piecewise linear planes is generated.

5. The dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method as described in claim 4, characterized in that, The aggregation cost surface includes a first aggregation cost surface and a second aggregation cost surface, which correspond to the first port and the second port of the microgrid, respectively. Each of the aggregate cost surfaces is composed of multiple piecewise linear planes, and the operating cost of the microgrid is greater than or equal to the value corresponding to each piecewise linear plane. The value corresponding to each piecewise linear plane is determined by a linear combination of the constant coefficient of the piecewise linear plane and the interactive active power and interactive reactive power of the corresponding port.

6. The dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method as described in claim 1, characterized in that, The step of simultaneously embedding the feasible PQ aggregation domain and the aggregation cost surface into the rolling dispatch model of the upper-level power grid includes: The feasible PQ aggregation domain is used as the feasible domain constraint of the upper-level power grid rolling dispatch model; The aggregated cost surface is used as the cost constraint of the upper-level power grid rolling scheduling model; The upper-level power grid rolling dispatch model aims to minimize the sum of the upper-level power grid's own operating costs and the microgrid's operating costs.

7. The dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method as described in claim 1, characterized in that, The internal scheduling of the microgrid according to the scheduling instructions includes: Using the scheduling instructions as boundary conditions and minimizing the internal operating cost of the microgrid as the objective, the dual-port microgrid model is used to solve the problem, thereby obtaining the output plan of each distributed energy source within the microgrid and updating the operating status of the distributed energy sources.

8. The dual-port microgrid PQ aggregation domain and aggregation cost surface rolling scheduling method as described in claim 1, characterized in that, The distributed energy source includes at least a micro gas turbine, an energy storage unit, and distributed photovoltaics; the operational constraints include at least the upper and lower limits of the output of the micro gas turbine and ramping constraints, the charging and discharging power and capacity constraints of the energy storage unit, and the output and curtailment constraints of the distributed photovoltaics.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 8.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method of any one of claims 1 to 8 through the computer program.

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