Power transmission and distribution system collaborative optimization scheduling method based on flexible domain of active power distribution network

Abstract

The invention discloses a power transmission and distribution system collaborative optimization scheduling method based on an active power distribution network flexible domain. Firstly, an active power distribution network flexibility double-layer aggregation framework containing multiple virtual power plants is constructed, and a dynamic flexible domain model based on heterogeneous resource equivalent aggregation is established; performing accurate internal approximation representation on the flexible domain through a boundary contraction algorithm; constructing a power transmission system economic dispatching model based on the flexible domain of the active power distribution network, and solving to obtain an optimal economic dispatching scheme of the power transmission system; and finally, constructing a power distribution system power distribution model based on the power transmission system scheduling scheme and a virtual power plant power distribution model based on the power distribution system scheduling scheme, and solving to obtain an optimal power distribution scheme of the power distribution system and the virtual power plant. According to the method, the dynamic flexible domain of the active power distribution network is depicted, and power transmission and distribution system cooperative scheduling optimization is performed based on the dynamic flexible domain, so that flexible source-load cooperative optimization under the dynamic interaction power local observation condition of the power transmission and distribution system boundary is realized, and the power sensing capability and scheduling comprehensive benefits of the system boundary are improved.

Description

Technical Field

[0001] This invention belongs to the field of power transmission and distribution optimization operation technology, specifically relating to a collaborative optimization scheduling method for power transmission and distribution systems based on the flexible domain of an active distribution network. Background Technology

[0002] Building a new power system with new energy sources as the mainstay has become a new direction for the development of the power industry. On the transmission side, the wide-area aggregation and development of centralized new energy sources enables cross-regional resource optimization. On the distribution network side, the plug-and-play nature of distributed new energy sources allows for the construction of local balancing units. Both will support and serve the clean and low-carbon transformation of energy supply. This also presents new theoretical and technical demands in areas such as system flexibility analysis, power balance, and coordinated grid development. Active distribution systems, characterized by a high proportion of distributed new energy access, have changed the traditional single-direction power flow pattern between transmission and distribution systems. Their functional positioning has also shifted from demand-driven loads to a flexible form that can be both sourced and loaded, enhancing power interaction, information coupling, and active support capabilities between transmission and distribution systems. When transmission networks experience congestion, the output characteristics of distributed resource clusters on the distribution network side can be precisely controlled to achieve flexible power correction at transmission nodes. When power limits are exceeded on distribution lines, the flexible resources on the transmission network side can be effectively utilized for voltage coordination control. This two-way interactive mechanism not only strengthens the dynamic support capabilities between power transmission and distribution systems, but also demonstrates significant advantages in reducing system investment costs, increasing the carrying capacity of new energy sources, and enhancing the grid's resilience through cross-voltage level resource collaborative optimization.

[0003] The collaborative optimization problem of power transmission and distribution systems is essentially a large-scale mixed-integer programming problem involving multiple time scales, multiple agents, and multiple variables. As the number of distribution systems interconnected with the transmission system increases, traditional centralized optimization methods become increasingly inadequate to handle the combinatorial explosion problem that arises during model solving. Furthermore, the transmission and distribution system is managed by investment and operation personnel at different levels, resulting in a degree of privacy in local information, with boundary nodes being the only points of information sharing. Existing optimization methods for transmission systems based on the equivalent load of the distribution system and for distribution systems based on the equivalent power source of the transmission system face the following challenges: 1) In transmission system optimization, the interconnected distribution systems typically need to be equivalent to time-varying loads. Since transmission system dispatchers cannot directly control the flexible load resources of the distribution system, excessive power demand at substation nodes may occur, leading to transmission line congestion. 2) In distribution system optimization, the transmission system is typically equivalent to a power source. Similarly, because distribution system operators cannot directly adjust the output of transmission system units, the voltage at boundary nodes remains constant, making it difficult to adjust the power flow distribution of the distribution system. When the system's active management capabilities or flexible resources are limited, distribution system optimization is further constrained, and the optimality of the resulting solution cannot be guaranteed. Furthermore, transmission and distribution systems belong to different investment and operation entities, lacking a unified energy management and dispatching agency, making centralized collaborative optimization of the transmission and distribution systems impossible.

[0004] In response to the problems mentioned in the background section and the shortcomings of existing technologies, the following specific issues urgently need to be addressed:

[0005] 1) Currently, power transmission and distribution system operators lack effective means of perception and visualization of the boundary interaction power and the flexibility aggregation status it represents during the collaborative process, making it difficult to achieve full-process monitoring of its operating points and limiting the operators' ability to control and manage the system's interactive status in real time.

[0006] 2) Existing flexible domain characterization systems mainly focus on the analysis of feasible intervals in static time sections, and fail to fully consider the dynamic coupling characteristics of multiple time scales brought about by the high proportion of distributed resource access. This makes it difficult for existing models and methods to accurately characterize the dynamic adjustment capabilities of distributed energy.

[0007] 3) Existing collaborative optimization scheduling methods for power transmission and distribution systems are difficult to evaluate the flexibility and coordination between systems. Although they can ensure that the resulting collaborative optimization scheme is economically optimal, they lack analysis of the complementary and mutually supportive characteristics of power transmission and distribution systems. Summary of the Invention

[0008] To address the shortcomings of existing technologies and improve upon them, this invention provides a collaborative optimization scheduling method and system for transmission and distribution systems based on the flexible domain of an active distribution network. This aims to enhance the power sensing capability at the boundaries of the transmission and distribution system and improve overall scheduling efficiency. To this end, this invention adopts the following technical solution.

[0009] This invention's scheduling method characterizes the flexibility of the active distribution network through a dynamic flexible domain and achieves coordinated optimization scheduling of the transmission and distribution system based on this dynamic flexible domain. For massive distributed energy access, a two-layer aggregation framework for the flexibility of the active distribution network, including multiple virtual power plants, is constructed. Based on the heterogeneous characteristics of distributed energy, a dynamic flexible domain model based on the equivalent aggregation of heterogeneous resources is established, and a boundary shrinkage algorithm is used to accurately approximate the flexible domain. Based on the dynamic flexible domain of the active distribution network, an economic scheduling model for the transmission system is constructed. Based on the optimal scheduling results of the transmission system, power allocation models for the distribution system layer and the virtual power plant layer are established to obtain the optimal scheduling scheme for the entire system.

[0010] In a first aspect, embodiments of this application provide a method for collaborative optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network, comprising the following steps:

[0011] Step (1) Construct a two-layer aggregation framework for active distribution network flexibility that includes multiple virtual power plants.

[0012] The lower layer of the active distribution network flexibility two-layer aggregation framework is the virtual power plant layer, which aggregates the flexibility of distributed energy through virtual power plants; the upper layer is the active distribution network layer, which integrates the flexibility of each virtual power plant in the lower layer with the flexibility of the distribution system under the premise of meeting network constraints on the distribution network side, and represents the overall flexibility of the distribution system at the substation node.

[0013] Step (2) Establish a dynamic flexible domain model based on the equivalent aggregation of heterogeneous resources.

[0014] The dynamic flexible domain model is divided into a virtual power plant layer and an active distribution network layer, corresponding to the flexible two-layer aggregation framework. In the virtual power plant layer, massive heterogeneous flexible resources are classified into equivalent generator models and equivalent energy storage models; in the active distribution network layer, while classifying heterogeneous flexible resources, the grid structure and safe and stable operation of the distribution system are ensured.

[0015] Step (3) The flexible domain is accurately approximated by the boundary shrinkage algorithm.

[0016] Combining the power characteristics of each distributed flexible resource with the output characteristics closely related to time (such as ramping and energy characteristics), a boundary contraction algorithm considering network security constraints and resource time coupling is proposed. By using an infeasible operating point search strategy and boundary operating point identification and positioning, the actual power range is approximated from the approximate polyhedron to obtain the flexible domain of the active power distribution system.

[0017] Step (4) Construct an economic dispatch model for the transmission system based on the flexible domain of the active distribution network, and solve for the optimal economic dispatch scheme of the transmission system.

[0018] By utilizing the flexible domain of the active distribution system as the power information transmitted on the distribution side, and taking the minimization of the total generation cost of the transmission system as the objective, an economic dispatch model for the transmission system is established using the output of renewable energy plants, load information, and power injected on the distribution side as optimization variables. The optimal economic dispatch scheme for the transmission system is obtained by solving the economic dispatch model.

[0019] Step (5) Construct a power allocation model for the distribution system based on the power transmission system scheduling scheme and a power allocation model for the virtual power plant based on the power transmission system scheduling scheme, and solve for the optimal power allocation scheme for the distribution system and the virtual power plant.

[0020] Based on the economic dispatch scheme of the transmission system generated by the economic dispatch model, minimizing the total generation cost of the distribution system is taken as the objective. A power allocation model for the distribution system is established using the output of distributed energy resources, load information, and injected power from virtual power plants as optimization variables. Similarly, based on the economic dispatch scheme, minimizing the total generation cost of virtual power plants is taken as the objective. A power allocation model for virtual power plants is established using the output of distributed energy resources and load information from virtual power plants as optimization variables. By solving the power allocation models of the distribution system and virtual power plants, the optimal power allocation scheme for the distribution system and virtual power plants is obtained.

[0021] Secondly, embodiments of this application provide a power transmission and distribution system collaborative optimization scheduling device based on the flexible domain of an active distribution network, comprising the following modules:

[0022] Framework building module: Used to build a two-layer aggregation framework for active distribution network flexibility that includes multiple virtual power plants.

[0023] The lower layer of the active distribution network flexibility two-layer aggregation framework is the virtual power plant layer, which aggregates the flexibility of distributed energy through virtual power plants; the upper layer is the active distribution network layer, which integrates the flexibility of each virtual power plant in the lower layer with the flexibility of the distribution system under the premise of meeting network constraints on the distribution network side, and represents the overall flexibility of the distribution system at the substation node.

[0024] Model building module: Used to build dynamic and flexible domain models based on the equivalent aggregation of heterogeneous resources.

[0025] The dynamic flexible domain model is divided into a virtual power plant layer and an active distribution network layer, corresponding to the flexible two-layer aggregation framework. In the virtual power plant layer, massive heterogeneous flexible resources are classified into equivalent generator models and equivalent energy storage models; in the active distribution network layer, while classifying heterogeneous flexible resources, the grid structure and safe and stable operation of the distribution system are ensured.

[0026] Flexible domain computation module: It performs an accurate inner approximation of the flexible domain through a boundary shrinkage algorithm.

[0027] Combining the power characteristics of each distributed flexible resource with the output characteristics closely related to time (such as ramping and energy characteristics), a boundary contraction algorithm considering network security constraints and resource time coupling is proposed. By using an infeasible operating point search strategy and boundary operating point identification and positioning, the actual power range is approximated from the approximate polyhedron to obtain the flexible domain of the active power distribution system.

[0028] Economic dispatch scheme solution module: By constructing an economic dispatch model of the transmission system based on the flexible domain of the active distribution network, the optimal economic dispatch scheme of the transmission system is obtained.

[0029] By utilizing the flexible domain of the active distribution system as the power information transmitted on the distribution side, and taking the minimization of the total generation cost of the transmission system as the objective, an economic dispatch model for the transmission system is established using the output of renewable energy plants, load information, and power injected on the distribution side as optimization variables. The optimal economic dispatch scheme for the transmission system is obtained by solving the economic dispatch model.

[0030] Power allocation scheme determination module: By constructing a power allocation model of the distribution system based on the power transmission system scheduling scheme and a power allocation model of the virtual power plant based on the power distribution system scheduling scheme, the optimal power allocation scheme of the distribution system and the virtual power plant is obtained.

[0031] Based on the economic dispatch scheme of the transmission system generated by the economic dispatch model, minimizing the total generation cost of the distribution system is taken as the objective. A power allocation model for the distribution system is established using the output of distributed energy resources, load information, and injected power from virtual power plants as optimization variables. Similarly, based on the economic dispatch scheme, minimizing the total generation cost of virtual power plants is taken as the objective. A power allocation model for virtual power plants is established using the output of distributed energy resources and load information from virtual power plants as optimization variables. By solving the power allocation models of the distribution system and virtual power plants, the optimal power allocation scheme for the distribution system and virtual power plants is obtained.

[0032] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory;

[0033] The memory is used to store computer programs.

[0034] When the processor executes the program stored in the memory, it implements any of the power transmission and distribution system collaborative optimization scheduling methods described in this application.

[0035] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the power transmission and distribution system collaborative optimization scheduling methods described in this application.

[0036] Fifthly, embodiments of this application provide a computer program product containing instructions that, when run on a computer, cause the computer to execute any of the power transmission and distribution system collaborative optimization scheduling methods described in this application.

[0037] The beneficial effects of this invention are as follows:

[0038] (1) In response to the phenomenon that the access of massive distributed flexible resources to the active distribution system makes it difficult to regulate the distribution system resources, a virtual power plant is introduced to integrate distributed resources. A two-layer aggregation framework for the flexibility of the active distribution network containing multiple virtual power plants is proposed to realize the effective integration and flexible aggregation of massive distributed resources.

[0039] (2) Considering that the power transmission and distribution system is only observable locally at the boundary nodes and the dynamic information interaction is limited, we study a method for generating the dynamic flexible domain of the active power distribution system. We adopt a power boundary contraction algorithm that considers network security constraints and resource timing coupling to achieve accurate situational awareness of the operating point location of the active power distribution system and rapid and accurate characterization of the dynamic flexible domain.

[0040] (3) Research the construction method of transmission and distribution coordinated economic dispatch model, establish the transmission system economic dispatch model based on the flexible domain of active distribution network and the power allocation model of distribution system and virtual power plant based on the optimal dispatch scheme, and realize the coordinated optimization of the transmission and distribution system dispatch scheme in terms of global economy and local executability. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other embodiments can be obtained based on these drawings.

[0042] Figure 1 This is a diagram illustrating the collaborative scheduling framework of the power transmission and distribution system according to an embodiment of the present invention.

[0043] Figure 2 This is a diagram illustrating the flexible two-layer aggregation framework of an embodiment of the present invention.

[0044] Figure 3 This is a flowchart illustrating the boundary shrinkage algorithm solution in an embodiment of the present invention.

[0045] Figure 4 To improve the IEEE 24-node transmission network structure diagram.

[0046] Figure 5 To improve the IEEE 33-node distribution network structure diagram.

[0047] Figure 6 This is a dynamic flexible domain result diagram for an active distribution network.

[0048] Figure 7 This is the optimal scheduling scheme for the power transmission system.

[0049] Figure 8 This is the optimal power allocation scheme for the power distribution system.

[0050] Figure 9 The optimal power allocation scheme for the virtual power plant. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art based on this application are within the scope of protection of this application.

[0052] like Figure 1 As shown, the present invention includes the following steps:

[0053] Step (1) Construct a two-layer aggregation framework for active distribution network flexibility with multiple virtual power plants. The lower layer of the active distribution network flexibility two-layer aggregation framework is the virtual power plant layer, which aggregates the flexibility of distributed energy through virtual power plants; the upper layer is the active distribution network layer, which integrates the flexibility of each virtual power plant in the lower layer with the flexibility of the distribution system under the premise of satisfying network constraints on the distribution network side, and represents the overall flexibility of the distribution system at the substation node.

[0054] Step (2) Establish a dynamic flexible domain model based on the equivalent aggregation of heterogeneous resources. The dynamic flexible domain model is divided into a virtual power plant layer and an active distribution network layer, corresponding to the two-layer aggregation framework of flexibility. In the virtual power plant layer, massive heterogeneous flexible resources are classified into equivalent generator models and equivalent energy storage models; in the active distribution network layer, while classifying heterogeneous flexible resources, the grid structure and safe and stable operation of the distribution system are ensured.

[0055] Step (3) proposes a boundary contraction algorithm to accurately approximate the flexible domain. Combining the output power characteristics of each distributed flexible resource and the output characteristics (such as ramp and energy characteristics) that are closely related to time sequence (listed or summarized), a power boundary contraction algorithm method considering network security constraints and resource time sequence coupling is proposed. Through the search strategy for infeasible operating points and the identification and location of boundary operating points, the actual power range is approximated from the approximate polyhedron to obtain the flexible domain of the active power distribution system.

[0056] Step (4) Construct an economic dispatch model for the transmission system based on the flexible domain of the active distribution network, and solve for the optimal economic dispatch scheme of the transmission system. Using the flexible domain of the active distribution system as the power information transmitted on the distribution side, and taking the minimization of the total generation cost of the transmission system as the objective, the economic dispatch model of the transmission system is established with the output of new energy power plants, load information, and power injected on the distribution side as optimization variables. By solving the economic dispatch model, the optimal economic dispatch scheme of the transmission system is obtained.

[0057] Step (5) Construct a power allocation model for the distribution system based on the transmission system dispatch scheme and a power allocation model for the virtual power plant based on the distribution system dispatch scheme, and solve for the optimal power allocation scheme for the distribution system and the virtual power plant. Based on the economic dispatch scheme of the transmission system generated by the economic dispatch model of the transmission system, the power allocation model of the distribution system is established with minimizing the total generation cost of the distribution system as the objective and the output of distributed energy, load information and injected power of the virtual power plant as optimization variables. Based on the economic dispatch scheme of the distribution system, the power allocation model of the virtual power plant is established with minimizing the total generation cost of the virtual power plant as the objective and the output of distributed energy and load information of the virtual power plant as optimization variables. By solving the two-stage power allocation model, the optimal power allocation scheme for the distribution system and the virtual power plant is obtained.

[0058] This application's technical solution focuses on power flexibility, achieving accurate characterization of the dynamic flexible domain of active distribution networks and coordinated optimization scheduling of transmission and distribution systems. For the accurate characterization of the dynamic flexible domain of active distribution networks, a two-layer aggregation framework for flexibility is proposed and a model is established, employing a boundary contraction algorithm to achieve accurate characterization of the flexible domain. For the coordinated optimization scheduling of transmission and distribution systems, the flexible domain of the active distribution network is used as transmission-distribution interaction information for optimized transmission network scheduling, and a power allocation scheme for distribution network and virtual power plant resources is implemented based on the scheduling scheme. This technical solution not only achieves accurate situational awareness of the operating point location of the active distribution system and rapid and accurate characterization of the dynamic flexible domain, but also achieves coordinated optimization of the transmission and distribution system scheduling scheme in terms of global economy and local executability.

[0059] In one possible implementation, the active distribution network flexibility domain is defined as the set of injected substation node power operating points that can be achieved by adjusting distributed flexible resources while ensuring system security. A two-layer aggregation framework for active distribution network flexibility, including multiple virtual power plants, is also included. Figure 2 A two-layer aggregation framework diagram for the flexibility of the active distribution network is presented, mainly consisting of an upper active distribution network layer and a lower virtual power plant layer. The upper active distribution network layer connects various virtual power plants and directly connects distributed energy resources and loads; the lower virtual power plant layer consists of multiple virtual power plants, each containing distributed energy resources and load equipment. These devices are first connected to the internal bus of the virtual power plant, and then connected to the active distribution network through gateway nodes.

[0060] In one possible implementation, based on the active distribution network flexibility two-layer aggregation framework, a dynamic flexible domain model based on the equivalent aggregation of heterogeneous resources is established. The model is divided into a virtual power plant layer and an active distribution network layer, and the specific modeling is as follows:

[0061] The constraints at the virtual power plant layer include power balance constraints, distributed generation operation constraints, load response constraints, and gate node power constraints:

[0062]

[0063] Among them, subscript Representative moment subscript Represents a virtual power plant connected to an active distribution network. ; , These represent the active and reactive power of the junction connection lines; (The rest of the text appears to be incomplete and requires further context.) , , , , , Representing virtual power plants It includes a collection of micro gas turbines, wind turbines, photovoltaic units, energy storage systems, flexible loads, and stationary loads; , , , , , These are the active power of micro gas turbines, wind turbines, photovoltaic units, energy storage systems, virtual power plant flexible loads, and virtual power plant stationary loads, respectively. , , These represent the reactive power of micro gas turbines, flexible loads in virtual power plants, and fixed loads in virtual power plants, respectively. , , , These are the active power boundary and the upper and lower limits of the ramping power of the wind turbine, respectively. , , , These are the active power boundary and the upper and lower limits of ramp power for photovoltaic units, respectively. , , , These are the upper and lower limits of the active and reactive power of the micro gas turbine, respectively. , These are the upper and lower limits of the ramp power of the micro gas turbine; , , , These represent the active power boundary and energy boundary of the energy storage system, respectively. The power factor angle for flexible loads; The active power boundary for the flexible load of the virtual power plant; , , , These are the upper and lower limits of active and reactive power for the interconnecting lines at the virtual power plant gateway nodes, respectively. The sampling time is defined as 1 hour in this example.

[0064] The constraints of the active distribution network layer include power flow equation constraints, distributed generation operation constraints, load response constraints, network security constraints, virtual power plant constraints, and substation power constraints.

[0065]

[0066] Among them, subscript Active distribution network representing the connection of the transmission network ; Subscript Represents active distribution network nodes ;gather Indicates active distribution network In and nodes The set of all nodes connected by branches; , , , , They represent active distribution networks. node A collection of connected virtual power plants, micro gas turbines, wind turbines, photovoltaic units, and energy storage systems; , Distribution network lines Active and reactive power transmitted upstream; , These represent the active and reactive power outputs of the virtual power plant, respectively. , These are the active and reactive power outputs of the substation, respectively. , , , These represent the active and reactive power of flexible loads and stationary loads, respectively. For active distribution network node The square of the voltage across; , Active distribution network line Resistance and reactance; The active power boundary for flexible loads; For active distribution network node The voltage on; , These are the upper and lower limits of the voltage, respectively. For active distribution network line The trend; The upper limit of the trend; , Representing virtual power plants The power vector consisting of active and reactive power at 24 hours. , Virtual power plants The coefficient matrix and right-hand side vector of the flexible field; , , , These are the upper and lower limits of active and reactive power for active distribution network substations, respectively.

[0067] In one possible implementation, for the heterogeneous flexible resources connecting the virtual power plant layer and the active distribution network layer, each flexible resource is classified and constructed into an equivalent generator model and an equivalent energy storage model based on its characteristics. The equivalent generator model includes wind power generation, photovoltaic power generation, micro gas turbines, flexible loads, and stationary loads; the equivalent energy storage model includes an energy storage system.

[0068] The flexible domain representation of the equivalent device is shown below:

[0069]

[0070]

[0071] in, Represents a set of times; superscript , These represent the equivalent generator and equivalent energy storage, respectively. and These represent the flexible domains of the equivalent generator and the equivalent energy storage, respectively. and These represent the power of the equivalent generator and the equivalent energy storage, respectively. and Representing time respectively The power of the equivalent generator and the equivalent energy storage; , , , They are time points The upper and lower limits of the power and the upper and lower limits of the ramp rate of the equivalent generator; , , and They are time points The upper and lower limits of power and energy for equivalent energy storage.

[0072] After integrating each distributed flexible resource into an equivalent generator model and an equivalent energy storage model, the overall flexible domain can be represented as follows: Among them, symbols Let represent the Minkowski sum, used for operations between sets, and its mathematical form is as follows:

[0073]

[0074] in, This represents the active power of a virtual power plant gateway node or an active distribution network substation.

[0075] In one possible implementation, at the lower virtual power plant layer, the virtual power plant aggregator collects the power characteristics of each distributed flexible resource within each virtual power plant, aggregates them using a boundary contraction algorithm, calculates the virtual power plant flexible domain at the gateway node, and reports it to the distribution system operator. At the upper active distribution network layer, the active distribution system integrates the virtual power plant flexible domains reported by each virtual power plant with the information of distributed resources directly connected to the active distribution network, and calculates and characterizes the flexible domain of the entire system at the substation node using a boundary contraction algorithm. (Boundary contraction algorithm...) Figure 3 A flowchart of the boundary shrinkage algorithm is provided, which includes the following steps:

[0076] Step 1: Define the geometric prototype.

[0077] The characterization of a flexible domain is essentially a high-dimensional polyhedron. The problem of projection onto a lower-dimensional space results in a projected polyhedron that represents a flexible domain. . and The tightening forms are represented as follows:

[0078]

[0079]

[0080] in, The decision variable vector consists of the active and reactive power of all distributed energy sources over all time periods. , This represents the coefficient matrix and right-hand side vector of the mapping relationship between aggregate power and decision variables; , Let be the coefficient matrix and right-hand side vector of all distributed energy constraints; and Let be the coefficient matrix and right-hand side vector of the flexible field.

[0081] The cluster that performs flexible resource aggregation still retains the original cluster's operating characteristics. For the equivalent generator model, the aggregated flexible domain can be further represented in the following compact form:

[0082]

[0083] Due to the solution and The exact value is an NP-hard problem, therefore a shape similar to... The geometric prototype is the polyhedron. Approximating it, the geometric prototype employs a power-energy boundary, described as:

[0084]

[0085] in, This is the constant matrix of the geometric prototype of the equivalent generator model; It is a vector composed of key parameters of the equivalent generator model, and its precise value is obtained by subsequent calculation.

[0086]

[0087]

[0088] in, It is the identity matrix; When used as a subscript, it indicates the matrix dimension.

[0089] Similarly, the geometric prototype of the equivalent energy storage model can be described as:

[0090]

[0091] in, This is the constant matrix of the geometric prototype of the equivalent energy storage model; It is a vector composed of key parameters of the equivalent energy storage model. Due to the difference between its precise value and the equivalent generator model... The solution is similar to that of the equivalent generator model; the solutions for subsequent steps 2 to 4 are derived from the equivalent generator model. Expand.

[0092]

[0093]

[0094] Step 2: Construct the initial polygon.

[0095] To correspond to a polyhedron The vector composed of key parameters is used to ensure that the polyhedron approximates the actual flexible domain while satisfying the constraints. Make it as large as possible. The flexible field parameter vector represents the vector for the k-th iteration, corresponding to the polyhedron formed after the k-th iteration. subscript This represents the k-th iteration. Starting from the initial polyhedron... Starting from this point, the initial parameter vector is obtained by solving the following linear optimization problem. .

[0096]

[0097] in, , These represent the maximum and minimum power values ​​in the actual flexible domain, respectively.

[0098] Step 3: Search for infeasible points.

[0099] Define infeasibility points Simultaneously satisfy and This is obtained by solving the following min-max problem.

[0100]

[0101] in, Let be the objective function of the min-max problem in the k-th iteration.

[0102] The min-max problem is optimized using the dual method, which is equivalent to KKT. The optimized problem is shown below:

[0103]

[0104] in, for The dual variable; for The dual variable; for The dual variable; It is a sufficiently large ( The constant of ); To limit constraints The upper and lower bounds of a constant matrix cannot be activated simultaneously; for The length of is used as a subscript to represent the vector dimension, and as a superscript to represent the vector space dimension; for A column vector of all 1s; for A two-dimensional column vector.

[0105]

[0106] Solving the equivalent min-max problem will yield the infeasible points. Substitute it into the constraints In this context, the constraint that takes the equality sign at that point is called the active constraint.

[0107] Step 4: Parameter adjustment.

[0108] By adjusting the flexible domain parameter vector To reduce the size of the polyhedron Boundary and exclude infeasible points The boundary near the infeasible point is obtained by solving the following optimization problem:

[0109]

[0110] in, For located On the boundary and with The point with the smallest distance is the solution to the optimization problem.

[0111] Let the set of row indicators for the activity constraint be denoted as As shown below:

[0112]

[0113] in, for The Row vectors; for The Each component.

[0114] Finally, adjust make become The new extreme point.

[0115]

[0116] in, For index set The value of is used as a subscript to represent the vector dimension and as a superscript to represent the vector space dimension. for A column vector of all 1s; for 2D binary column vector; It is a binary variable; For index set Local indexes in the database.

[0117] In obtaining Then, the infeasibility search is repeated for the next iteration until the algorithm converges, i.e., the infeasibility search fails, and the final flexible domain is output.

[0118] In one possible implementation, the objective function of the economic dispatch model for the transmission system based on the flexible domain of the active distribution network is to minimize the total cost of the transmission system, and its mathematical expression is:

[0119]

[0120] in, Indicates the total cost of the power transmission network; subscript Represents a power transmission network node ;gather , These are sets of time points and transmission network nodes, respectively; , , , Representing the nodes of the transmission network A collection of connected active distribution networks, thermal power plants, wind farms, and photovoltaic power stations; Active power transmitted in an active distribution network; , , These represent the active power of thermal power plants, wind farms, and photovoltaic power plants, respectively. For power transmission network nodes Active power of flexible loads; , These are the active power boundaries for wind farms and photovoltaic power plants, respectively. For electricity purchase costs; The production cost of thermal power plants; , These are the penalty costs for wind and solar power curtailment, respectively, for wind farms and solar power plants. This refers to the load shedding cost for flexible loads.

[0121] The constraints of the economic dispatch model for transmission systems based on the flexible domain of active distribution networks include:

[0122] 1) Power flow equality constraints of the transmission system;

[0123]

[0124]

[0125]

[0126] Among them, set For the nodes in the power transmission network The set of all nodes connected by the branch; For power transmission lines Active power transmitted upstream; For power transmission network nodes Active power of a stationary load; For power transmission network nodes The voltage phase angle; For power transmission lines The resistance on it.

[0127] 2) Power plant operation constraints;

[0128]

[0129]

[0130]

[0131] in, , , , These are the upper and lower limits of active power and ramp power for thermal power plants, respectively. , , , These are the upper and lower limits of ramp power for wind farms and photovoltaic power stations, respectively.

[0132] 3) Load response constraints;

[0133]

[0134] in, This represents the active power boundary for flexible loads.

[0135] 4) Network security constraints;

[0136]

[0137]

[0138] in, For power transmission network nodes The voltage on; , These are the upper and lower limits of the voltage, respectively. , For power transmission lines Upper and lower limits of active power transmitted uplink.

[0139] 5) Active distribution network constraints;

[0140]

[0141] in, Represents active distribution network The active power vector; , Active distribution network The coefficient matrix and right-hand side vector of the flexible field.

[0142] In one possible implementation, the objective function of the power allocation model for the distribution system based on the transmission system scheduling scheme is to minimize the total cost of the distribution system, and its mathematical expression is:

[0143]

[0144] in, For active distribution network Total cost; set For active distribution network A collection of nodes; , These are the production costs of micro gas turbines and energy storage systems, respectively. , These are the penalty costs for wind and solar power curtailment, respectively, for wind turbines and solar power units.

[0145] The constraints of the power allocation model for the distribution system based on the power transmission system dispatching scheme include:

[0146] 1) Power flow equality constraints of the power distribution system;

[0147]

[0148]

[0149]

[0150] 2) Operational constraints of distributed generation;

[0151]

[0152]

[0153]

[0154]

[0155] 3) Load response constraints;

[0156]

[0157] 4) Network security constraints;

[0158]

[0159]

[0160] 5) Substation power constraints;

[0161]

[0162] in, Active distribution network after economic dispatch of transmission network node The optimal output active power of the connected substation.

[0163] 6) Virtual power plant constraints;

[0164]

[0165] In one possible implementation, the objective function of the virtual power plant power allocation model based on the power distribution system scheduling scheme is to minimize the total cost of the virtual power plant, and its mathematical expression is:

[0166]

[0167] in, For virtual power plants The total cost.

[0168] The constraints of the virtual power plant power allocation model based on the power distribution system dispatch scheme include:

[0169] 1) Power balance constraints;

[0170]

[0171]

[0172] 2) Operational constraints of distributed generation;

[0173] Distributed generation operation constraints in the same power allocation model of the distribution network.

[0174] 3) Load response constraints;

[0175]

[0176] 4) Power constraints at gateway nodes;

[0177]

[0178] in, Virtual power plants after active distribution network economic dispatch The optimal output active power of the junction node connecting line.

[0179] The following describes this embodiment in more detail with reference to a specific implementation plan. This embodiment uses an improved IEEE 24-node transmission network and an IEEE 33-node distribution network for simulation. Figure 4 and Figure 5 The grid structures of the transmission and distribution systems are presented separately. The transmission network includes 10 thermal power plants, 3 wind farms, and 3 photovoltaic power plants. The distribution network is configured with three different flexible resource combination schemes, as shown in Table 1. The virtual power plants are also configured with three different flexible resource combination schemes, as shown in Table 2. The simulation parameters of the power plants and distributed generator units connected to the system are shown in Table 3.

[0180] Table 1 Flexible resource allocation for each active distribution network

[0181] wind turbine Photovoltaic units micro gas turbine Energy storage system Active distribution network 1 3 3 3 2 Active distribution network 2 4 4 3 2 Active distribution network 3 3 3 5 2

[0182] Table 2 Flexible Resource Allocation for Each Virtual Power Plant

[0183] wind turbine Photovoltaic units micro gas turbine Energy storage system Fixed load Flexible load Virtual Power Plant 1 3 3 4 2 3 3 Virtual Power Plant 2 3 2 6 2 3 3 Virtual Power Plant 3 4 3 3 2 3 3

[0184] Table 3 Simulation parameters for power plants and distributed generator sets

[0185] Upper limit of power / energy thermal power plant 155~591MW / - wind farm 200MW / - Photovoltaic power station 200MW / - wind turbine 1~4.5MW / - Photovoltaic units 0.5~2.5MW / - micro gas turbine 0.5~2.5MW / - Energy storage system 0.1~0.5MW / 0.4~2MWh

[0186] Figure 6The paper presents the boundary evolution patterns of the dynamic flexible domain of an active distribution network under three flexible resource allocation schemes. Visualization results clearly reveal the impact of different resource allocations on the flexible domain boundary: during the midday peak of photovoltaic (PV) output, the lower power limit of the active distribution network's flexible domain decreases. This is due to the enhanced back-feeding capability of PV resources, enabling the distribution network to transmit surplus power to the transmission network, highlighting the positive contribution of renewable energy to system flexibility. Conversely, during peak load periods, the upper power limit of the flexible domain increases accordingly, reflecting the characteristic that the distribution network needs to absorb more power from the transmission network to meet local load demands. Active distribution networks with more micro gas turbines consistently maintain the widest adjustment range, verifying the crucial supporting role of rapidly adjustable resources for the flexible domain. Under the premise of ensuring network security constraints, the boundary contraction algorithm accurately approximates the flexible domain, improving computational accuracy and significantly reducing computation time compared to traditional algorithms. The results fully demonstrate that the proposed method achieves a good balance between computational accuracy and efficiency when characterizing the flexible domain of an active distribution network, exhibiting good performance and efficiency.

[0187] Figure 7 This paper presents an improved IEEE 24-node transmission network with combined unit and active distribution network output under optimal dispatch instructions. Renewable energy power plants exhibit significant temporal complementarity: photovoltaic power plants reach full capacity at midday, while wind farms output peak power at night, with only a limited curtailment of 42.6 MW of solar power occurring at time 11, indicating that the dispatch model effectively coordinates the consumption of renewable resources. Traditional thermal power plants play a crucial peak-shaving role, increasing their output to the limit during peak load periods (times 13-18), effectively filling the power gap caused by renewable energy fluctuations. When the transmission network has high load demand, the active distribution network injects power into the transmission network, demonstrating its reverse power supply capability; conversely, when local power plants generate surplus power, the active distribution network absorbs power from the transmission network, highlighting its flexible load characteristics. Ultimately, the total cost of the transmission system was optimized to 16.809 million yuan, validating the significant effectiveness of the flexible domain-based transmission system dispatch model in improving economic efficiency.

[0188] Figure 8The power allocation results of Active Distribution Network 1 under transmission dispatch instructions are presented. Wind and solar resources follow the principle of "priority consumption," with solar power output being higher during midday and wind power output higher at night. The micro gas turbine outputs 2.5MW at full capacity during peak electricity price periods, effectively reducing the cost of purchased electricity. The energy storage system plays a crucial regulatory role, charging 0.05MW of energy at off-peak times (9) and discharging 0.05MW at peak times (16) to participate in peak shaving, achieving spatiotemporal energy transfer. The load management strategy reflects a moderately conservative approach, only cutting off a total of 2.27MW of load during the most constrained network period (19-20), maximizing power supply reliability. Through these optimization measures, the total cost of Active Distribution Network 1 is reduced to 49,800 yuan, highlighting the economic value brought by resource synergy.

[0189] Figure 9 Further detailed internal optimization allocation for Virtual Power Plant 1 is presented. The micro gas turbine operates at full output of 0.5MW during peak local load periods, effectively compensating for fluctuations in wind and solar power output. Renewable energy output is precisely matched to local load demand, maximizing the utilization of local energy resources. The energy storage system executes a refined control strategy, charging 0.01MW of stored energy during periods of local surplus electricity demand (11) and discharging 0.01MW during peak load periods (16) to participate in demand response. Ultimately, the total cost of Virtual Power Plant 1 is controlled at 13,500 yuan, validating the cost advantages of hierarchical dispatching within the virtual power plant framework.

[0190] In summary, the method proposed in this application effectively integrates massive heterogeneous distributed flexible resources, solving the problem of difficult resource regulation in distribution networks under high-proportion renewable energy access. Simultaneously, the boundary shrinkage algorithm achieves an accurate internal approximation of the dynamic flexible domain of the active distribution network, significantly improving the power sensing capability and computational efficiency at the transmission-distribution interaction boundary. Furthermore, this method realizes optimal economic dispatch of the transmission system and power allocation of the distribution system and virtual power plants, ensuring the global economy and local executability of the dispatch scheme, and providing an efficient and feasible technical path for transmission-distribution coordinated dispatch under new power systems.

[0191] This application also provides a power transmission and distribution system collaborative optimization scheduling device based on the flexible domain of an active distribution network, including the following modules:

[0192] Framework building module: Used to build a two-layer aggregation framework for active distribution network flexibility that includes multiple virtual power plants.

[0193] The lower layer of the active distribution network flexibility two-layer aggregation framework is the virtual power plant layer, which aggregates the flexibility of distributed energy through virtual power plants; the upper layer is the active distribution network layer, which integrates the flexibility of each virtual power plant in the lower layer with the flexibility of the distribution system under the premise of meeting network constraints on the distribution network side, and represents the overall flexibility of the distribution system at the substation node.

[0194] Model building module: Used to build dynamic and flexible domain models based on the equivalent aggregation of heterogeneous resources.

[0195] The dynamic flexible domain model is divided into a virtual power plant layer and an active distribution network layer, corresponding to the flexible two-layer aggregation framework. In the virtual power plant layer, massive heterogeneous flexible resources are classified into equivalent generator models and equivalent energy storage models; in the active distribution network layer, while classifying heterogeneous flexible resources, the grid structure and safe and stable operation of the distribution system are ensured.

[0196] Flexible domain computation module: It performs an accurate inner approximation of the flexible domain through a boundary shrinkage algorithm.

[0197] Combining the power characteristics of each distributed flexible resource with the output characteristics closely related to time (such as ramping and energy characteristics), a boundary contraction algorithm considering network security constraints and resource time coupling is proposed. By using an infeasible operating point search strategy and boundary operating point identification and positioning, the actual power range is approximated from the approximate polyhedron to obtain the flexible domain of the active power distribution system.

[0198] Economic dispatch scheme solution module: By constructing an economic dispatch model of the transmission system based on the flexible domain of the active distribution network, the optimal economic dispatch scheme of the transmission system is obtained.

[0199] By utilizing the flexible domain of the active distribution system as the power information transmitted on the distribution side, and taking the minimization of the total generation cost of the transmission system as the objective, an economic dispatch model for the transmission system is established using the output of renewable energy plants, load information, and power injected on the distribution side as optimization variables. The optimal economic dispatch scheme for the transmission system is obtained by solving the economic dispatch model.

[0200] Power allocation scheme determination module: By constructing a power allocation model of the distribution system based on the power transmission system scheduling scheme and a power allocation model of the virtual power plant based on the power distribution system scheduling scheme, the optimal power allocation scheme of the distribution system and the virtual power plant is obtained.

[0201] Based on the economic dispatch scheme of the transmission system generated by the economic dispatch model, minimizing the total generation cost of the distribution system is taken as the objective. A power allocation model for the distribution system is established using the output of distributed energy resources, load information, and injected power from virtual power plants as optimization variables. Similarly, based on the economic dispatch scheme, minimizing the total generation cost of virtual power plants is taken as the objective. A power allocation model for virtual power plants is established using the output of distributed energy resources and load information from virtual power plants as optimization variables. By solving the power allocation models of the distribution system and virtual power plants, the optimal power allocation scheme for the distribution system and virtual power plants is obtained.

[0202] This application also provides an electronic device, including a processor and a memory.

[0203] The memory is used to store computer programs.

[0204] When the processor executes a program stored in the memory, it implements any of the methods described in this application.

[0205] In one possible implementation, the electronic device of this application embodiment further includes a communication interface and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0206] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0207] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0208] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0209] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0210] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements any of the methods described in this application.

[0211] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform any of the methods described in this application.

[0212] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially 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 this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0213] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0214] The various embodiments in this specification are described in a related manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other.

[0215] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application are included within the scope of protection of this application.

Claims

1. A method for collaborative optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network, characterized in that, Includes the following steps: Step (1) Construct a two-layer aggregation framework for active distribution network flexibility that includes multiple virtual power plants; The lower layer of the active distribution network flexibility two-layer aggregation framework is the virtual power plant layer, which aggregates the flexibility of distributed energy through virtual power plants; the upper layer is the active distribution network layer, which integrates the flexibility of each virtual power plant in the lower layer with the flexibility of the distribution system under the premise of meeting network constraints on the distribution network side, and represents the overall flexibility of the distribution system at the substation node. Step (2) Establish a dynamic flexible domain model based on the equivalent aggregation of heterogeneous resources; The dynamic flexible domain model is divided into a virtual power plant layer and an active distribution network layer, which corresponds to the flexible two-layer aggregation framework. In the virtual power plant layer, massive heterogeneous flexible resources are classified into equivalent generator models and equivalent energy storage models. In the active distribution network layer, while classifying heterogeneous flexible resources, the grid structure and safe and stable operation of the distribution system are ensured. Step (3) Use the boundary shrinkage algorithm to perform an accurate inner approximation of the flexible domain; Combining the power characteristics of each distributed flexible resource and the output characteristics closely related to time sequence, a boundary shrinkage algorithm considering network security constraints and resource time sequence coupling is proposed. Through the search strategy of infeasible operating points and the identification and location of boundary operating points, the actual power range is approximated by the approximate polyhedron to obtain the flexible domain of the active power distribution system. Step (4) Construct an economic dispatch model for the transmission system based on the flexible domain of the active distribution network, and solve for the optimal economic dispatch scheme of the transmission system; By utilizing the flexible domain of the active distribution system as the power information transmitted on the distribution system side, and taking the minimization of the total generation cost of the transmission system as the objective, an economic dispatch model for the transmission system is established with the output of renewable energy power plants, load information, and power injected on the distribution system side as optimization variables. By solving the economic dispatch model, the optimal economic dispatch scheme for the transmission system is obtained. Step (5) Construct a power allocation model for the distribution system based on the power transmission system scheduling scheme and a power allocation model for the virtual power plant based on the power distribution system scheduling scheme, and solve for the optimal power allocation scheme for the distribution system and the virtual power plant; Based on the economic dispatch scheme of the transmission system generated by the economic dispatch model, the goal is to minimize the total generation cost of the distribution system. A power allocation model for the distribution system is established using distributed energy output, load information, and virtual power plant injection power as optimization variables. Similarly, based on the economic dispatch scheme, the goal is to minimize the total generation cost of the virtual power plant. A power allocation model for the virtual power plant is established using distributed energy output and load information as optimization variables. By solving the power allocation models of the distribution system and the virtual power plant, the optimal power allocation scheme between the distribution system and the virtual power plant is obtained.

2. The method for coordinated optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network according to claim 1, characterized in that, The active distribution network flexibility two-layer aggregation framework with multiple virtual power plants includes an upper active distribution network layer and a lower virtual power plant layer. The upper active distribution network layer connects each virtual power plant and directly connects distributed energy resources and loads. The lower virtual power plant layer consists of multiple virtual power plants, each of which contains distributed energy resources and load equipment. These devices are first connected to the internal bus of the virtual power plant and then connected to the active distribution network through gateway nodes.

3. The method for coordinated optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network according to claim 1, characterized in that, The constraints of the virtual power plant layer in the dynamic flexible domain model based on the equivalent aggregation of heterogeneous resources include power balance constraints, distributed generation operation constraints, load response constraints, and gate node power constraints; the constraints of the active distribution network layer in the dynamic flexible domain model based on the equivalent aggregation of heterogeneous resources include power flow equation constraints, distributed generation operation constraints, load response constraints, network security constraints, virtual power plant constraints, and substation power constraints.

4. The method for coordinated optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network according to claim 3, characterized in that, For the heterogeneous flexible resources connecting the virtual power plant layer and the active distribution network layer, each flexible resource is classified and constructed into an equivalent generator model and an equivalent energy storage model according to its characteristics. The equivalent generator model includes wind power generation, photovoltaic power generation, micro gas turbine, flexible load and stationary load; the equivalent energy storage model includes energy storage system. The flexible domain representation of the equivalent device is shown below: in, Represents a set of times; superscript , These represent the equivalent generator and equivalent energy storage, respectively. and These represent the flexible domains of the equivalent generator and the equivalent energy storage, respectively. and These represent the power of the equivalent generator and the equivalent energy storage, respectively. and Representing time respectively The power of the equivalent generator and the equivalent energy storage; , , , They are time points The upper and lower limits of the power and the upper and lower limits of the ramp rate of the equivalent generator; , , and They are time points The upper and lower limits of power and energy of equivalent energy storage; After integrating each distributed flexible resource into an equivalent generator model and an equivalent energy storage model, the overall flexible domain can be represented as follows: Among them, the symbol Let represent the Minkowski sum, used for operations between sets, and its mathematical form is as follows: in, This represents the active power of a virtual power plant gateway node or an active distribution network substation.

5. The method for coordinated optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network according to claim 2, characterized in that, In the lower virtual power plant layer, the virtual power plant aggregator collects the power characteristics of each distributed flexible resource within each virtual power plant, aggregates them using a boundary shrinking algorithm, calculates the virtual power plant flexible domain at the gateway node, and reports it to the distribution system operator. In the upper active distribution network layer, the active distribution system integrates the virtual power plant flexible domains reported by each virtual power plant and the status of distributed resources directly connected to the active distribution network, and calculates and characterizes the flexible domain of the entire system at the substation node using a boundary shrinking algorithm.

6. The method for collaborative optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network according to claim 1 or 5, characterized in that, The boundary shrinkage algorithm specifically includes the following steps: Step 1: Define the geometric prototype; The characterization of a flexible domain is essentially a high-dimensional polyhedron. The problem of projection onto a lower-dimensional space results in a projected polyhedron that represents a flexible domain. ; and The tightening forms are represented as follows: in, The decision variable vector consists of the active and reactive power of all distributed energy sources over all time periods. , This represents the coefficient matrix and right-hand side vector of the mapping relationship between aggregate power and decision variables; , Let be the coefficient matrix and right-hand side vector of all distributed energy constraints; and Let be the coefficient matrix and right-hand side vector of the flexible field; The cluster that performs flexible resource aggregation still retains the original cluster's operating characteristics. For the equivalent generator model, the aggregated flexible domain can be further represented in the following compact form: Due to the solution and The exact value is an NP-hard problem, therefore a shape similar to... The geometric prototype is the polyhedron. Approximating it, the geometric prototype employs a power-energy boundary, described as: in, This is the constant matrix of the geometric prototype of the equivalent generator model; It is a vector composed of key parameters of the equivalent generator model, and its precise value is obtained by subsequent calculation; in, It is the identity matrix; When used as a subscript, it indicates the matrix dimension; Similarly, the geometric prototype of the equivalent energy storage model can be described as: in, This is the constant matrix of the geometric prototype of the equivalent energy storage model; It is a vector composed of key parameters of the equivalent energy storage model. Due to the difference between its precise value and the equivalent generator model... The solution is similar to that of the equivalent generator model; the solutions for subsequent steps 2 to 4 are derived from the equivalent generator model. Expand; Step 2: Construct the initial polygon; To correspond to a polyhedron The vector composed of key parameters is used to ensure that the polyhedron approximates the actual flexible domain while satisfying the constraints. As large as possible; The flexible field parameter vector represents the vector for the k-th iteration, corresponding to the polyhedron formed after the k-th iteration. subscript This indicates the k-th iteration; starting from the initial polyhedron Starting from this point, the initial parameter vector is obtained by solving the following linear optimization problem. ; in, , These represent the maximum and minimum power values ​​in the actual flexible domain, respectively. Step 3: Search for infeasible points; Define infeasibility points Simultaneously satisfy and This is obtained by solving the following min-max problem; in, Let be the objective function of the min-max problem in the k-th iteration; The min-max problem is optimized using the dual method, which is equivalent to KKT. The optimized problem is shown below: in, for The dual variable; for The dual variable; for The dual variable; It is a sufficiently large constant; To limit constraints The upper and lower bounds of a constant matrix cannot be activated simultaneously; for The length of is used as a subscript to represent the vector dimension, and as a superscript to represent the vector space dimension; for A column vector of all 1s; for 2D binary column vector; Solving the equivalent min-max problem will yield the infeasible points. Substitute it into the constraints In this context, the constraint that takes the equality sign at that point is the active constraint; Step 4: Parameter adjustment; By adjusting the flexible field parameter vector To reduce the size of the polyhedron Boundary and exclude infeasible points The boundary near the infeasible point is obtained by solving the following optimization problem: in, For located On the boundary and with The point with the smallest distance is the solution to the optimization problem; Let the set of row indicators for the activity constraint be denoted as As shown below: in, for The Row vectors; for The One component; Finally, adjust make become The new extreme point; in, For index set The value of is used as a subscript to represent the vector dimension and as a superscript to represent the vector space dimension. for A column vector of all 1s; for 2D binary column vector; It is a binary variable; For index set Local indexes in; In obtaining Then, the infeasibility search is repeated for the next iteration until the algorithm converges, i.e., the infeasibility search fails, and the final flexible domain is output.

7. The method for coordinated optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network according to claim 1, characterized in that, The objective function of the economic dispatch model for transmission systems based on the flexible domain of active distribution networks is to minimize the total cost of the transmission system. Its mathematical expression is as follows: in, Indicates the total cost of the power transmission network; subscript Represents a power transmission network node ;gather , These are sets of time points and transmission network nodes, respectively; , , , Representing the nodes of the transmission network A collection of connected active distribution networks, thermal power plants, wind farms, and photovoltaic power stations; Active power transmitted in an active distribution network; , , These represent the active power of thermal power plants, wind farms, and photovoltaic power plants, respectively. For power transmission network nodes Active power of flexible loads; , These are the active power boundaries for wind farms and photovoltaic power plants, respectively. For electricity purchase costs; The production cost of thermal power plants; , These are the penalty costs for wind and solar power curtailment, respectively, for wind farms and solar power plants. The cost of shedding loads for flexible loads; The constraints of the economic dispatch model for transmission systems based on the flexible domain of active distribution networks include: transmission system power flow equality constraints, power plant operation constraints, load response constraints, network security constraints, and active distribution network constraints.

8. The method for coordinated optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network according to claim 1, characterized in that, The objective function of the power allocation model for a distribution system based on a power transmission system dispatching scheme is to minimize the total cost of the distribution system. Its mathematical expression is as follows: in, For active distribution network Total cost; set For active distribution network A collection of nodes; , These are the production costs of micro gas turbines and energy storage systems, respectively. , The respective penalties for wind and solar curtailment for wind turbines and solar power units; (The sentence is incomplete and ends abruptly.) , , , , They represent active distribution networks. node A collection of connected virtual power plants, micro gas turbines, wind turbines, photovoltaic units, and energy storage systems; The active power output of the virtual power plant; , , , These are the active power of micro gas turbines, wind turbines, photovoltaic units, and energy storage systems, respectively. This represents the upper limit of the active power boundary for wind turbine generators. This represents the upper limit of the active power boundary for photovoltaic (PV) generators. The active power of the flexible load; The cost of shedding loads for flexible loads; The constraints of the power allocation model of the distribution system based on the power transmission system dispatch scheme include: power flow equality constraints of the distribution system, distributed generation operation constraints, load response constraints, network security constraints, substation power constraints, and virtual power plant constraints.

9. The method for coordinated optimization scheduling of power transmission and distribution systems based on the flexible domain of an active distribution network according to claim 1, characterized in that, The objective function of the virtual power plant power allocation model based on the power distribution system dispatch scheme is to minimize the total cost of the virtual power plant, and its mathematical expression is: in, For virtual power plants Total cost; set , , , , Representing virtual power plants It includes a collection of micro gas turbines, wind turbines, photovoltaic units, energy storage systems, and flexible loads; , , , , These are the active power of micro gas turbines, wind turbines, photovoltaic units, energy storage systems, and virtual power plant flexible loads, respectively. The cost of shedding loads for flexible loads; The constraints of the virtual power plant power allocation model based on the power distribution system dispatch scheme include: power balance constraints, distributed generation operation constraints, load response constraints, and gate node power constraints.

10. A power transmission and distribution system collaborative optimization scheduling device based on the flexible domain of an active distribution network, characterized in that, Includes the following modules: Framework building module: Used to build a two-layer aggregation framework for active distribution network flexibility that includes multiple virtual power plants; The lower layer of the active distribution network flexibility two-layer aggregation framework is the virtual power plant layer, which aggregates the flexibility of distributed energy through virtual power plants; the upper layer is the active distribution network layer, which integrates the flexibility of each virtual power plant in the lower layer with the flexibility of the distribution system under the premise of meeting network constraints on the distribution network side, and represents the overall flexibility of the distribution system at the substation node. Model building module: used to build dynamic and flexible domain models based on the equivalent aggregation of heterogeneous resources; The dynamic flexible domain model is divided into a virtual power plant layer and an active distribution network layer, which corresponds to the flexible two-layer aggregation framework. In the virtual power plant layer, massive heterogeneous flexible resources are classified into equivalent generator models and equivalent energy storage models. In the active distribution network layer, while classifying heterogeneous flexible resources, the grid structure and safe and stable operation of the distribution system are ensured. Flexible domain computation module: performs accurate interior approximation representation of the flexible domain through a boundary shrinkage algorithm; Combining the power characteristics of each distributed flexible resource and the output characteristics closely related to time sequence, a boundary shrinkage algorithm considering network security constraints and resource time sequence coupling is proposed. Through the search strategy of infeasible operating points and the identification and location of boundary operating points, the actual power range is approximated by the approximate polyhedron to obtain the flexible domain of the active power distribution system. Economic dispatch scheme solution module: By constructing an economic dispatch model of the transmission system based on the flexible domain of the active distribution network, the optimal economic dispatch scheme of the transmission system is obtained; By utilizing the flexible domain of the active distribution system as the power information transmitted on the distribution system side, and taking the minimization of the total generation cost of the transmission system as the objective, an economic dispatch model for the transmission system is established with the output of renewable energy power plants, load information, and power injected on the distribution system side as optimization variables. By solving the economic dispatch model, the optimal economic dispatch scheme for the transmission system is obtained. Power allocation scheme determination module: By constructing a power allocation model of the distribution system based on the power transmission system scheduling scheme and a power allocation model of the virtual power plant based on the power distribution system scheduling scheme, the optimal power allocation scheme of the distribution system and the virtual power plant is obtained. Based on the economic dispatch scheme of the transmission system generated by the economic dispatch model, the goal is to minimize the total generation cost of the distribution system. A power allocation model for the distribution system is established using distributed energy output, load information, and virtual power plant injection power as optimization variables. Similarly, based on the economic dispatch scheme, the goal is to minimize the total generation cost of the virtual power plant. A power allocation model for the virtual power plant is established using distributed energy output and load information as optimization variables. By solving the power allocation models of the distribution system and the virtual power plant, the optimal power allocation scheme between the distribution system and the virtual power plant is obtained.

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