Distributed collaborative optimization method and system for multi-level flexible interconnected transmission and distribution network

Abstract

The application discloses a kind of distributed collaborative optimization method and system of multilevel flexible interconnected transmission and distribution network, it is related to the collaborative optimization field of multilevel transmission and distribution network, including according to the physical connection relationship of flexible interconnection equipment and the network level of belonging to determine joint solution range: the controllable power exchange relationship formed by flexible direct current is included into transmission side joint optimization model, and the distribution network of the controllable power exchange relationship formed by SOP is included into distribution side joint optimization model;Active, reactive power exchange power of transmission side and distribution side between transmission and distribution interface is boundary coupling variable, and the interface operating state is consistent by distributed iteration is realized.On this basis, conventional unit, wind power, flexible direct current, distributed photovoltaic and SOP etc. are coordinated to adjustable resource.The application realizes the collaborative operation of different levels of flexible resources by transmission layer cross-regional power mutual aid and distribution layer flexible power regulation, and improves system economic operation and network operating state.

Description

Technical Field

[0001] This application relates to the field of collaborative optimization technology for multi-level power transmission and distribution networks, and in particular to a distributed collaborative optimization method and system for multi-level flexible interconnected power transmission and distribution networks. Background Technology

[0002] With the increasing penetration of new energy sources such as wind power and distributed photovoltaics in transmission and distribution networks, the power system is gradually forming a multi-regional, multi-level, and flexibly interconnected network structure. The operational states of different network levels and power supply areas influence each other, making traditional independent optimization methods focused on a single network or level insufficient to meet the collaborative operation requirements under conditions of high-proportion new energy access. Existing transmission and distribution network collaborative optimization technologies mainly include two categories: centralized optimization and distributed coordinated optimization. Centralized optimization models the transmission network, distribution network, and adjustable resources in a unified manner, directly considering the coupling relationships between different networks. However, it requires complete sharing of internal models and operational data on both sides, and the model size and data interaction volume increase accordingly with the increase in network scale, participating entities, and optimization time periods. Distributed optimization decomposes the transmission and distribution sides into independent sub-problems, using the boundary quantities of the transmission and distribution interfaces as coupling variables for alternating iterative solutions. This can reduce the direct exchange of information between the internal networks of the transmission and distribution sides to a certain extent and is a typical implementation method for current transmission and distribution collaborative optimization.

[0003] At the transmission layer, power sharing between different AC transmission areas can be achieved through controllable power electronic devices such as flexible DC transmission. At the distribution layer, flexible interconnection devices such as smart soft switches (Soft Open Point, SOP) are widely used to connect different feeders, power supply areas, or active distribution networks. Existing technologies have formed technical routes such as distributed coordination at the transmission and distribution boundaries, flexible DC power regulation between transmission networks, and SOP flexible interconnection optimization between distribution networks. However, when the above-mentioned multiple interconnection relationships exist simultaneously in the same multi-level transmission and distribution network, the following shortcomings still exist: there is a lack of a mechanism to reasonably determine the distributed optimization boundary for different flexible interconnection relationships; the distributed coordination optimization of flexible interconnection networks at the same level is highly complex, which is not conducive to the simplification and expansion of the distributed optimization model; independent optimization on the transmission side and the distribution side cannot guarantee the consistency of the operating state of the transmission and distribution interface; and centralized joint optimization cannot take into account the data autonomy and large-scale computing needs of the transmission and distribution operators. Summary of the Invention

[0004] The purpose of this application is to provide a distributed collaborative optimization method and system for multi-level flexible interconnected power transmission and distribution networks, thereby solving the aforementioned problems existing in the prior art.

[0005] To achieve the above objectives, this application provides a distributed collaborative optimization method for multi-level flexible interconnected power transmission and distribution networks, comprising the following steps: S1: Obtain network data and determine the joint solution range for the transmission and distribution sides based on the physical connection relationship of the flexible interconnection equipment; S2: Establish a joint optimization model for flexible interconnected transmission networks. With the goal of minimizing the sum of the generation cost of conventional units and the cost of wind curtailment penalty within the transmission-side set, incorporate the operational constraints of flexible DC interconnection as internal constraints of the transmission side into the joint optimization model of the transmission side. Jointly determine the output of conventional units, wind power output, AC network power flow, flexible DC transmission power, and the local active and reactive boundary coupling variables of each transmission and distribution interface on the transmission side. S3: Establish a joint optimization model for flexible interconnected distribution networks, with the goal of minimizing the sum of line losses and node voltage deviations within the distribution-side set. The SOP flexible interconnection constraint is incorporated into the joint optimization model as an internal constraint of the distribution side. Jointly determine the distributed photovoltaic output, distribution network power flow, node voltage, line current, active and reactive power of the SOP port, and the local active and reactive power boundary coupling variables of the distribution side at the transmission and distribution interface. S4: Construct consistency constraints for the local boundary coupling variables established by the transmission and distribution sides for the same transmission and distribution interface, and perform distributed coordination based on the alternating direction multiplier method. Construct an augmented Lagrangian function and decompose it into a transmission side subproblem and a distribution side subproblem for alternating iterative solution. During the iteration process, update the Lagrangian multipliers and the penalty factor of the alternating direction multiplier method until the original residual and the dual residual satisfy the convergence condition. S5: After meeting the convergence conditions, output the operating results of conventional units and wind power in each transmission network, flexible DC transmission power, active and reactive power exchange at each transmission and distribution interface, as well as photovoltaic output, node voltage, line power flow and SOP port adjustment in each distribution network, and generate corresponding dispatch instructions or operation evaluation indicators according to application requirements.

[0006] Preferably, determining the joint solution range for the transmission and distribution sides based on the physical connection relationship of the flexible interconnection devices specifically includes: AC transmission networks that are directly connected via flexible DC and whose transmission power needs to be jointly determined in the optimization process will be included in the same transmission-side joint optimization scope; Distribution networks that are directly connected via SOPs and whose port power needs to be jointly determined in the optimization process will be included in the same distribution side joint optimization scope. The transmission side and the distribution side establish a distributed coordination relationship only through the boundary coupling variables corresponding to each transmission and distribution interface.

[0007] Preferably, in S1, for the same group of physical exchange power of transmission and distribution interface m in time period t, local active power boundary coupling variables and local reactive power boundary coupling variables are set on the transmission side and the distribution side respectively, and the same positive power direction and power reference are used on both sides; the boundary coupling variable of the m-th transmission and distribution interface in time period t is expressed as: ; ; Where z is a two-dimensional boundary coupling variable arranged in [P,Q]; For the transmission network side boundary coupling variables, For the boundary coupling variables of the distribution network side, P and Q represent the active and reactive power of the interface, respectively; m is the index of the transmission and distribution interface; t=1,2,…,T is the time period index; the superscripts TN and DN represent the transmission network side and the distribution network side, respectively. , These are the sets of transmission networks and distribution networks participating in this joint optimization, respectively, where n and d are the indices of the transmission network and distribution network, respectively. , These are the transmission and distribution interface sets belonging to transmission network n and distribution network d, respectively.

[0008] Preferably, the objective is to minimize the sum of the generation cost of conventional units within the transmission-side cluster and the cost of wind curtailment penalty, expressed as: ; in, The original objective function on the transmission side is... , These are the costs of generating electricity from conventional units and the cost of wind curtailment penalties, respectively. The power generation cost of a conventional generating unit is expressed as follows: ; in, Let g be the unit active power generation cost. For the active power output of unit g. The duration of a single time period; The cost of wind curtailment penalties is expressed as follows: ; in, Let w be the penalty coefficient for the unit amount of wind curtailment of the wind turbine generator. , These are available wind power and actual output, respectively.

[0009] Preferably, the operational constraints for flexible DC interconnection include: Flexible DC transmission generates active power injection at the endpoints of the connected power grid, as shown below: ; The flexible DC transmission power satisfies the operating boundary, as expressed as: ; in, It is a flexible DC collection, For flexible DC collection index, Active power for flexible DC transmission; For endpoint position and injection direction coefficients, the receiving end is set to 1, the sending end is set to -1, and non-access nodes are set to 0. Flexible DC The transmitted active power during time period t, This is the corresponding lower power limit. This represents the corresponding power limit.

[0010] Preferably, the objective is to minimize the sum of line losses and node voltage deviations within the distribution-side aggregation, expressed as: ; in, The original objective function for the distribution side is... , These are the line loss target and the node voltage deviation target, respectively. The line loss target is: ; in, For loss weight, For line resistance, The square of the line current amplitude. Let d be the set of lines in the distribution network. The target for node voltage deviation is: ; in, As the voltage deviation weight, The square of the node voltage magnitude, The square of the reference voltage amplitude, For distribution network The set of nodes.

[0011] Preferably, in step S3, the SOP flexible interconnection constraint includes the SOP active power constraint and the SOP capacity constraint. For a two-port SOP connecting distribution network port A and port B, the active power constraint of SOP is expressed as: ; in, , These represent the active power injected by SOP into distribution network port A and distribution network port B during time period t, respectively. Positive values ​​indicate that the power is injected into the distribution network, while negative values ​​indicate that the power is absorbed from the distribution network. , These represent the non-negative active power losses at the two ports, respectively. and These are the loss coefficients for port A and port B, respectively. and Reactive power injection is performed on ports A and B, respectively. The active and reactive power of each SOP port are both limited by the port's rated capacity: ; in, The rated apparent capacity of the SOP; For a general form involving multiple ports, the SOP active power balance and port losses are expressed as follows: ; in, Index for SOP devices; Let be the set of ports for device s; 'a' is the port index. , These represent the active and reactive power injected into the distribution network by port a during time period t, respectively. The active power loss at port a; Port loss coefficient; The active and reactive power of each SOP port are both limited by the port's rated capacity: .

[0012] Preferably, a consistency constraint is constructed for the local boundary coupling variables established by the transmission side and the distribution side for the same transmission and distribution interface, expressed as: ; in, , Let the decision variable vectors be those for the transmission and distribution sides. , The feasible regions are composed of the operational constraints on the transmission side and the distribution side, respectively. This refers to the set of all transmission and distribution interfaces connected to the transmission network n and the distribution network d. Let m be the overall objective function for transmission and distribution coordination; for the same transmission and distribution interface m and the same time period t, For transmission-side local boundary coupling variables, These are local boundary coupling variables on the distribution side. Both consist of active and reactive power exchange quantities at the interface and are defined with the same positive power direction and power reference. Since the variables on both sides correspond to the exchange power of the same physical interface, the consistency constraint of the transmission and distribution interface is satisfied. This ensures that the descriptions of the operating status of the transmission and distribution interfaces are consistent between the transmission side and the distribution side.

[0013] The augmented Lagrangian function is constructed as follows: ; in, Let k be the Lagrange multiplier vector of the kth round. >0 is the penalty factor, and ||·|2 is the second-order norm; in the k-th iteration, after fixing the interface variables of the distribution side in the previous round, the subproblem of the transmission side is expressed as: ; in, For the k-th iteration, the local boundary coupling variable on the distribution side; After fixing the current-round interface variables on the transmission side, the distribution side subproblem is represented as: ; in, For the (k+1)th iteration, the local boundary coupling variable on the transmission side is denoted as .

[0014] Preferably, during the iteration process, the Lagrange multipliers and the penalty factor for the alternating direction multiplier method are updated until the original residual and the dual residual satisfy the convergence condition, specifically including: Initialize the transmission and distribution interface boundary coupling variables, Lagrange multipliers, penalty factors and their upper and lower limits, residual balance parameters, convergence threshold, and maximum number of iterations; in the k-th round, solve the transmission side sub-problem and send the results. Then solve the distribution side problem and return. The original residuals are expressed as: ; in, The maximum original residual for all transmission and distribution interfaces, ||·|| ∞ It is an infinite norm; The dual residuals corresponding to two consecutive rounds of changes in the interface variable are: ; in, The maximum dual residual for all transmission and distribution interfaces; Lagrange multiplier vectors are updated as follows: ; in, , These are the Lagrange multiplier vectors before and after the update, respectively; The iteration ends when both the original residual and the dual residual simultaneously satisfy the threshold. ; in, , These are the thresholds for the original residual and the dual residual, respectively; if convergence fails and the maximum number of iterations has not been reached, the problem returns to the transmission side subproblem. The penalty factor for the next round is adaptively adjusted based on the relative magnitudes of the original residual and the dual residual: ; Where μ is the residual balance threshold; , These are the magnification / reduction factors; , These are the upper and lower limits of the penalty factor; the penalty factor is increased when the original residual is too large, and decreased when the dual residual is too large; otherwise, they remain unchanged. The updated... Used for the next iteration.

[0015] A distributed collaborative optimization system for a multi-level flexible interconnected power transmission and distribution network adopts a distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network, including: a data acquisition and joint solution range determination module, a transmission side joint optimization module, a distribution side joint optimization module, a transmission and distribution boundary distributed coordination module, and an optimization result output module; The data acquisition and joint solution range determination module is used to acquire network topology, load and new energy forecast data of each transmission network and distribution network, as well as the access location and operating parameters of flexible DC, transmission and distribution interfaces and SOPs; determine the joint solution range of the transmission side and distribution side according to the physical connection relationship of flexible interconnection equipment, and establish the correspondence between the transmission and distribution interfaces and their respective transmission and distribution networks.

[0016] The transmission-side joint optimization module is used to establish a joint optimization model for the flexible interconnected transmission network that participates in collaborative optimization. It jointly determines the output of conventional units, wind power output, AC network power flow, flexible DC transmission power, and local active and reactive boundary coupling variables of the transmission and distribution interface. Among them, the flexible DC interconnection constraint is directly incorporated into the joint optimization model as an internal constraint of the transmission side.

[0017] The distribution-side joint optimization module is used to establish a joint optimization model for the flexible interconnected distribution network that participates in collaborative optimization. It jointly determines the distributed photovoltaic output, distribution network power flow, node voltage, line current, active and reactive power at the SOP port, and the boundary coupling variables of active and reactive power at the local distribution side of the transmission and distribution interface. Among them, the SOP flexible interconnection constraint is directly incorporated into the joint optimization model as an internal constraint of the distribution side.

[0018] The transmission and distribution boundary distributed coordination module is used to construct consistency constraints for the local boundary coupling variables established by the transmission side and the distribution side for the same transmission and distribution interface, and to perform distributed coordination based on the alternating direction multiplier method. In each iteration, the module passes the corresponding boundary variables, Lagrange multipliers and penalty factors to the transmission side joint optimization module and the distribution side joint optimization module, obtains the active and reactive power of the interface obtained from the solutions on both sides, calculates the original residual and dual residual, updates the Lagrange multipliers, and adjusts the penalty factor according to the preset rules until the convergence condition is met.

[0019] The optimization result output module is used to output the operating results of conventional units and wind power in each transmission network, flexible DC transmission power, active and reactive power exchange at each transmission and distribution interface, photovoltaic output, node voltage, line power flow and SOP port adjustment of each distribution network after the convergence conditions are met, and to generate corresponding dispatch instructions or operation evaluation indicators according to application requirements.

[0020] Therefore, the distributed collaborative optimization method and system for a multi-level flexible interconnected power transmission and distribution network described above have the following beneficial effects: (1) This application determines the joint solution range based on the physical connection relationship of the flexible interconnection equipment and the network level to which it belongs. The flexible DC and SOP interconnection constraints are embedded in the joint optimization model of the transmission side and the distribution side respectively. Only the transmission and distribution interface is used as the distributed coordination boundary to reduce unnecessary boundary variables and coordination process.

[0021] (2) This application enables the flexible DC and the connected transmission network, and the SOP and the connected distribution network to be jointly optimized at the same level, so as to achieve coordinated determination of the operating status of the flexible interconnection equipment and the power flow status of the network.

[0022] (3) This application uses the active and reactive power exchange power of the transmission and distribution interface as boundary coupling variables, and realizes the consistency coordination between the transmission side and the distribution side based on ADMM to ensure that both sides obtain the same interface operation status.

[0023] (4) This application maintains the relative independence of the internal network models and operating data of the Transmission System Operator (TSO) and the Distribution System Operator (DSO), and only uses transmission and distribution boundary variables and coordination quantities for information exchange, thereby reducing the need for cross-entity internal data sharing.

[0024] (5) This application coordinates conventional units, wind power, flexible DC, distributed photovoltaic and SOP under a unified multi-time period optimization framework, and adaptively adjusts the Alternating Direction Method of Multipliers (ADMM) penalty factor through residual balancing to improve the adaptability of multi-type flexible resource coordinated operation and distributed solution.

[0025] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to this application. Figure 2 This is a system topology diagram in an embodiment of this application; Figure 3 This is a graph showing the ADMM residual convergence curve in an embodiment of this application; Figure 4 This is a diagram showing the flexible DC transmission power curve between transmission networks A and B in this embodiment of the application. Figure 5 This is a diagram showing the SOP transmission power curve between distribution networks A and B in this embodiment of the application. Detailed Implementation

[0027] The following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0028] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning as understood by a person of ordinary skill in the art to which this application pertains.

[0029] The terms "comprising" or "including," as used in this application, mean that the element preceding the term encompasses the element listed after it, and do not exclude the possibility of encompassing other elements as well. The terms "inner," "outer," "upper," and "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this application, unless otherwise expressly specified and limited, the term "attached," etc., should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0030] Example 1: A distributed collaborative optimization method for multi-level flexible interconnected power transmission and distribution networks, such as... Figure 1As shown, this example illustrates two AC transmission networks, two transmission-distribution interfaces, two active distribution networks, a flexible DC transmission device connecting the two transmission networks at both ends, a two-port SOP connecting the two distribution networks, and multiple optimization time periods. Each transmission network is connected to an active distribution network through a transmission-distribution interface. The two transmission networks are laterally interconnected via flexible DC, and the two distribution networks are laterally interconnected via SOPs. The listed formulas are in set form, and the number of transmission networks, distribution networks, flexible DC, transmission-distribution interfaces, SOPs, and optimization time periods can all be expanded. The process includes the following steps: S1: Obtain network data and determine the joint solution range for the transmission and distribution sides based on the physical connection relationship of the flexible interconnection equipment; Acquire topology, load and new energy forecast data for each transmission and distribution network within the optimization period; obtain access location, operating boundary and rated parameters of conventional units, flexible DC, transmission and distribution interfaces and SOPs; and check the consistency of dimensions, positive power direction and time resolution.

[0031] The joint solution scope for different network levels is determined based on the access location and physical connection relationship of the flexible interconnection devices. For AC transmission networks directly connected by flexible DC and whose transmission power needs to be jointly determined in this optimization, they are included in the same transmission-side joint optimization scope; for distribution networks directly connected by SOPs and whose port power needs to be jointly determined in this optimization, they are included in the same distribution-side joint optimization scope. Flexible DC transmission power and SOP port power are used as internal decision variables within their respective levels and are not separately used as distributed consistency variables between transmission networks or distribution networks. Distributed coordination relationships are established between the transmission and distribution sides only through the boundary coupling variables corresponding to each transmission and distribution interface.

[0032] For the transmission and distribution interface m During the period t For the same set of physical exchange power, local boundary coupling variables are set in both the transmission network model and the distribution network model to which they belong. The variables on both sides are local representations of the active and reactive power exchange quantities at the same transmission and distribution interface in the two autonomous models. Active and reactive power flowing from the transmission network to the distribution network are defined as positive, and the same power reference is used: ; ; Where z is the number of [ P , Q Two-dimensional boundary coupling variables arranged in a specific pattern; P , Q These represent the active and reactive power of the interface, respectively. m For the input / output interface index; t =1,2,…, T For time period indexing, T can be 24; the superscripts TN and DN represent the transmission network side and the distribution network side, respectively; , These are the transmission network set and distribution network set participating in this joint optimization. n , d These are indexes for the transmission network and distribution network, respectively. , They belong to the power transmission network n and distribution network d The set of transmission and distribution interfaces.

[0033] S2: Establish a joint optimization model for flexible interconnected transmission networks. With the goal of minimizing the sum of the generation cost of conventional units and the cost of wind curtailment penalty within the transmission-side set, incorporate the operational constraints of flexible DC interconnection as internal constraints of the transmission side into the joint optimization model of the transmission side. Jointly determine the output of conventional units, wind power output, AC network power flow, flexible DC transmission power, and the local active and reactive boundary coupling variables of each transmission and distribution interface on the transmission side. The objective, aiming to minimize the sum of the generation cost of conventional units within the transmission-side cluster and the cost of wind curtailment penalty, is expressed as: ; in, The original objective function on the transmission side is... , These are the costs of generating electricity from conventional units and the cost of wind curtailment penalties, respectively. The power generation cost of a conventional generating unit is expressed as follows: ; in, Let g be the unit active power generation cost. For the active power output of unit g. The duration of a single time period; The cost of wind curtailment penalties is expressed as follows: ; in, Let w be the penalty coefficient for the unit amount of wind curtailment of the wind turbine generator. , These are available wind power and actual output, respectively.

[0034] Operational constraints for flexible DC interconnects include: Flexible DC transmission generates active power injection at the endpoints of the connected power grid, as shown below: ; The flexible DC transmission power satisfies the operating boundary, as expressed as: ; in, It is a flexible DC collection, For flexible DC collection index, Active power for flexible DC transmission; The endpoint position and injection direction coefficients are set to 1 for the receiving end, -1 for the sending end, and 0 for non-access nodes; the sum of the non-zero coefficients of the lossless two-end model is 0; when considering losses, the injection and loss variables of the sending and receiving ends can be set separately. Flexible DC The transmitted active power during time period t, This is the corresponding lower power limit. This represents the corresponding power limit.

[0035] The flexible DC transmission power and its endpoint injection variables are solved synchronously with the power flow variables of the connected AC transmission network in the same transmission side joint optimization model. The flexible DC endpoint injection relationship and transmission power operation constraints are directly satisfied as internal constraints of the transmission side. No consistency variables, Lagrange multipliers or independent iterative processes are set for the AC transmission network connected to the flexible DC.

[0036] Transmission side operating constraints: (1) Generator output constraints: The active and reactive power outputs of conventional generating units meet the following requirements: ; in, , and , These are the upper and lower limits of the unit's active and reactive power output, respectively. This is a collection of conventional generating units.

[0037] (2) Wind power output constraints: The actual output of the wind turbine meets the following requirements: ; in, For wind turbines w During the period t Available power; A collection of wind turbine units.

[0038] (3) Node voltage and phase angle constraints: The voltage, phase angle, and slack line of each transmission node in the power grid satisfy the following: ; ; in, i For transmission node indexing, For power transmission network n The set of nodes; , These represent the per-unit value of the node voltage amplitude and the phase angle, respectively. , These are the upper and lower limits of the voltage. i 0,n For power transmission network n The balance node.

[0039] (4) Boundary coupling variable constraints of transmission and distribution interface: The local active and reactive power variables at the transmission and distribution interface on the transmission network side satisfy the following: ; The underlined and overlined quantities represent the corresponding lower and upper power limits, respectively.

[0040] (5) Node power injection and node power balance constraints: A linear AC power flow model is adopted, with nodes... i The active and reactive power injections in the network are respectively: ; The corresponding network reactive power injection is: ; Taking into account conventional generating units, wind power, loads, transmission and distribution interfaces, and flexible DC terminal injection, the active and reactive power balance equations at the nodes are as follows: ; in, G ij,n , B ij,n Let be the real and imaginary parts of the elements of the node admittance matrix; , For node load; For the position indication of the transmission and distribution interface, the interface m Connect to node i The value is 1 if the condition is met, otherwise it is 0. This is active power injection at the flexible DC endpoint. The positive direction of the interface power is from the transmission network to the distribution network, so it is treated as an outflow term in the transmission node balancing.

[0041] (6) AC line power flow and capacity constraints: AC lines ( i , j The active and passive currents are: ; The line apparent power meets the capacity constraint: ; in, g ij,n , b ij,n For the real and imaginary parts of the series admittance of the line, , For the node iFlow to Node j The trend of meritorious and ineffective actions This represents the maximum line capacity. For power transmission network n The collection of communication routes.

[0042] S3: Establish a joint optimization model for flexible interconnected distribution networks, with the goal of minimizing the sum of line losses and node voltage deviations within the distribution-side set. The SOP flexible interconnection constraint is incorporated into the joint optimization model as an internal constraint of the distribution side. Jointly determine the distributed photovoltaic output, distribution network power flow, node voltage, line current, active and reactive power of the SOP port, and the local active and reactive power boundary coupling variables of the distribution side at the transmission and distribution interface. The objective, which aims to minimize the sum of line losses and node voltage deviations within the distribution-side aggregation, is expressed as: ; in, The original objective function for the distribution side is... , These are the line loss target and the node voltage deviation target, respectively. The line loss target is: ; in, For loss weight, For line resistance, The square of the line current amplitude. Let d be the set of lines in the distribution network. The target for node voltage deviation is: ; in, As the voltage deviation weight, The square of the node voltage magnitude, The square of the reference voltage amplitude, For distribution network The set of nodes.

[0043] Preferably, in step S3, the SOP flexible interconnection constraint includes the SOP active power constraint and the SOP capacity constraint. For a two-port SOP connecting distribution network port A and port B, the active power constraint of SOP is expressed as: ; in, , These represent the active power injected by SOP into distribution network port A and distribution network port B during time period t, respectively. Positive values ​​indicate that the power is injected into the distribution network, while negative values ​​indicate that the power is absorbed from the distribution network. , These represent the non-negative active power losses at the two ports, respectively. and These are the loss coefficients for port A and port B, respectively. and Reactive power injection is performed on ports A and B, respectively. The active and reactive power of each SOP port are both limited by the port's rated capacity: ; in, The rated apparent capacity of the SOP; For a general form involving multiple ports, the SOP active power balance and port losses are expressed as follows: ; in, Index for SOP devices; Let be the set of ports for device s; 'a' is the port index. , These represent the active and reactive power injected into the distribution network by port a during time period t, respectively. The active power loss at port a; Port loss coefficient; The active and reactive power of each SOP port are both limited by the port's rated capacity: .

[0044] The active, reactive, and loss variables of each port of the SOP are solved synchronously with the power flow variables of the connected distribution network in the same distribution side joint optimization model. The power balance, loss, and capacity constraints of the SOP port are directly satisfied as internal constraints of the distribution side, without setting consistency variables, Lagrange multipliers, or independent iterative processes for each port of the SOP between distribution networks.

[0045] Distribution side operating constraints: (1) Constraints on distributed photovoltaic output: The actual output of photovoltaic equipment does not exceed the available power for the corresponding time period: ; in, , Photovoltaic equipment p The actual output and available power; For distribution network d A collection of photovoltaic equipment.

[0046] (2) Boundary coupling variable constraints of transmission and distribution interface: The local active and reactive power variables at the transmission and distribution interface on the distribution network side satisfy the following: ; Among them, the underlined and overlined quantities are the upper and lower limits of the power of the distribution side interface, respectively.

[0047] (3) Active and reactive power balance constraints at nodes: For distribution network lines i to j ,node j The active and reactive power balance equations are as follows: ; in, For nodes j The downstream node set, For access nodes j Photovoltaic equipment collection, For access nodes j SOP port set; For the position indication of the transmission and distribution interface, the interface m Connect to node j The value is 1 if the condition is met, otherwise it is 0. , This refers to the power injected into the distribution network from the SOP port. , For node load.

[0048] (4) Power flow, voltage drop and capacity constraints of distribution lines: The line voltage drop meets the following requirements: ; The line power, voltage, and current satisfy a second-order cone relaxation: ; Node voltage, line current, and line apparent power meet the operating boundaries: ; in, x ij,d For line reactance, V̲ i,d , V̄ i,d These are the upper and lower limits of the node voltage amplitude. I ij,d , S̄ ij,d These are the line current and the upper limit of apparent power, respectively.

[0049] S4: Construct consistency constraints for the local boundary coupling variables established by the transmission and distribution sides for the same transmission and distribution interface, and perform distributed coordination based on the alternating direction multiplier method. Construct an augmented Lagrangian function and decompose it into a transmission side subproblem and a distribution side subproblem for alternating iterative solution. During the iteration process, update the Lagrangian multipliers and the penalty factor of the alternating direction multiplier method until the original residual and the dual residual satisfy the convergence condition. Based on the inclusion of flexible DC interconnection constraints and SOP flexible interconnection constraints into the joint optimization models of the transmission and distribution sides, the transmission and distribution interface that crosses the autonomous boundaries of the transmission and distribution sides is taken as the distributed coordination boundary. For the local active and reactive power boundary coupling variables established by the transmission and distribution sides for the same transmission and distribution interface, the alternating direction multiplier method is used for consistency coordination, so that the two sides can obtain a consistent transmission and distribution interface operation state through limited boundary information interaction while maintaining the relative independence of their internal models and operating data.

[0050] The original objective functions and operational constraints of the transmission and distribution sides are uniformly expressed as the following collaborative optimization model for the power transmission and distribution network: ; in, x TN , x DN Let the decision variable vectors be those for the transmission and distribution sides. , The feasible region is composed of the operational constraints on the transmission side and the distribution side, respectively. To connect to the power transmission network n and distribution network d The complete set of all transmission and distribution interfaces. J This is the overall objective function for coordinated power transmission and distribution. For the same power transmission and distribution interface... m and the same period t Local boundary coupling variables are retained on both the transmission and distribution sides. and Both consist of active and reactive power exchange quantities at the interface, and specify the same positive power direction and power reference. Since the variables on both sides correspond to the exchange power of the same physical interface, the consistency constraint of the transmission and distribution interface must be satisfied: This ensures that the descriptions of the operating status of the transmission and distribution interfaces are consistent between the transmission side and the distribution side.

[0051] The ADMM method is used to solve the above collaborative optimization model with consistency constraints at the distribution interface. An augmented Lagrange multiplier vector and a quadratic penalty term are introduced to construct the augmented Lagrange function: ; in, For the first k Round Lagrange multiplier vectors, rk >0 is the penalty factor, and ||·|2 is the second-order norm. Separating the augmented Lagrangian function according to the subject to which the variable belongs yields the following two autonomous subproblems.

[0052] In the k In the round of iteration, after fixing the interface variables of the distribution side in the previous round, the subproblem on the transmission side is: ; The transmission side problem retains the original objective of step two. J TN and feasible domain It also includes coordination terms related to the interface variables of the previous round on the distribution side.

[0053] in, Let Lagrange multiplier vectors be the active and reactive power consistency constraints. r k As a penalty factor. After fixing the current-round interface variables on the transmission side, the distribution side subproblem is: ; The two subproblems retain their original objectives and feasible regions, exchanging only the active and reactive boundary coupling variables, Lagrange multipliers, and penalty factors at the transmission and distribution interfaces.

[0054] ADMM Iterative Update and Convergence Criterion: Initialize the boundary coupling variables, Lagrange multipliers, and penalty factors of the transmission and distribution interface. r 0 and its upper and lower limits, residual balance parameters, convergence threshold and maximum number of iterations, initial penalty factor r 0 can be set according to the interface's rated capacity and the magnitude of the objective function. k First solve the transmission side problem and then send it. Then solve the distribution side problem and return. The original residuals are: ; in, r k+1 The maximum original residual for all transmission and distribution interfaces, ||·|| ∞ It is an infinite norm.

[0055] The dual residuals corresponding to two consecutive rounds of changes in the interface variable are: ; in, s k+1 This represents the maximum dual residual of all transmission and distribution interfaces.

[0056] Lagrange multiplier vectors are updated as follows: ; in, , These are the Lagrange multiplier vectors before and after the update, respectively, and their two components correspond to the active and reactive power consistency constraints.

[0057] The iteration ends when both the original residual and the dual residual simultaneously satisfy the threshold. ; in, e pri , e dual These are the thresholds for the original residual and the dual residual, respectively, which can be set to 10. -4 If the problem fails to converge and the maximum number of iterations has not been reached, the problem reverts to the transmission side problem. The maximum number of iterations k in this application is... max 500 is acceptable.

[0058] To improve the consistency convergence performance of ADMM under different operating scenarios, the penalty factor for the next round is adaptively adjusted based on the relative magnitudes of the original residual and the dual residual: ; in, m This is the residual balance threshold, which can be set to 10 in practical applications; t inc , t dec For magnification and reduction factors, in practical applications t inc , t dec Option 2 is acceptable; r min , r max These are the upper and lower limits of the penalty factor. They increase when the original residuals are large. r When the dual residual is large, decrease r All other cases remain unchanged, the updated r k+1 Used for the next iteration.

[0059] S5: After meeting the convergence conditions, output the operating results of conventional units and wind power in each transmission network, flexible DC transmission power, active and reactive power exchange at each transmission and distribution interface, as well as photovoltaic output, node voltage, line power flow and SOP port adjustment in each distribution network, and generate corresponding dispatch instructions or operation evaluation indicators according to application requirements.

[0060] Specifically, after the ADMM iteration meets the preset convergence conditions, it outputs the active and reactive power output of conventional units in each transmission network at each time period, the wind power intake and curtailment, the flexible DC transmission power, the active and reactive boundary coupling variables of each transmission and distribution interface, the photovoltaic output, node voltage, line power flow, current and loss of each distribution network, and the active, reactive and loss of the SOP port.

[0061] For online control systems, the results can be converted into timing setpoints for conventional units, flexible DC, photovoltaic inverters, and SOPs in each transmission network; for offline analysis systems, the results can be further output as follows: power generation cost, wind curtailment, flexible DC switching power, network loss, minimum node voltage, line load factor, and algorithm convergence curve for each transmission network.

[0062] In practical applications, the voltage amplitude of each transmission network node can be limited to 0.94 to 1.06 pu; the voltage amplitude of each distribution network node can be limited to 0.90 to 1.10 pu, and the corresponding squared voltage amplitude boundaries are adopted in the model. The flexible DC power range, SOP port capacity and loss coefficient, active and reactive power range of transmission and distribution interfaces, line capacity and target weight are set according to the rated parameters of the equipment and operating requirements.

[0063] Example 2: A distributed collaborative optimization system for a multi-level flexible interconnected power transmission and distribution network adopts a distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network, including: a data acquisition and joint solution range determination module, a transmission side joint optimization module, a distribution side joint optimization module, a transmission and distribution boundary distributed coordination module, and an optimization result output module; The data acquisition and joint solution range determination module is used to acquire network topology, load and new energy forecast data of each transmission network and distribution network, as well as the access location and operating parameters of flexible DC, transmission and distribution interfaces and SOPs; determine the joint solution range of the transmission side and distribution side according to the physical connection relationship of flexible interconnection equipment, and establish the correspondence between the transmission and distribution interfaces and their respective transmission and distribution networks.

[0064] The transmission-side joint optimization module is used to establish a joint optimization model for the flexible interconnected transmission network that participates in collaborative optimization. It jointly determines the output of conventional units, wind power output, AC network power flow, flexible DC transmission power, and local active and reactive boundary coupling variables of the transmission and distribution interface. Among them, the flexible DC interconnection constraint is directly incorporated into the joint optimization model as an internal constraint of the transmission side.

[0065] The distribution-side joint optimization module is used to establish a joint optimization model for the flexible interconnected distribution network that participates in collaborative optimization. It jointly determines the distributed photovoltaic output, distribution network power flow, node voltage, line current, active and reactive power at the SOP port, and the boundary coupling variables of active and reactive power at the local distribution side of the transmission and distribution interface. Among them, the SOP flexible interconnection constraint is directly incorporated into the joint optimization model as an internal constraint of the distribution side.

[0066] The transmission and distribution boundary distributed coordination module is used to construct consistency constraints for the local boundary coupling variables established by the transmission side and the distribution side for the same transmission and distribution interface, and to perform distributed coordination based on the alternating direction multiplier method. In each iteration, the module passes the corresponding boundary variables, Lagrange multipliers and penalty factors to the transmission side joint optimization module and the distribution side joint optimization module, obtains the active and reactive power of the interface obtained from the solutions on both sides, calculates the original residual and dual residual, updates the Lagrange multipliers, and adjusts the penalty factor according to the preset rules until the convergence condition is met.

[0067] The optimization result output module is used to output the operating results of conventional units and wind power in each transmission network, flexible DC transmission power, active and reactive power exchange at each transmission and distribution interface, photovoltaic output, node voltage, line power flow and SOP port adjustment of each distribution network after the convergence conditions are met, and to generate corresponding dispatch instructions or operation evaluation indicators according to application requirements.

[0068] Example 3: To verify the effectiveness of the distributed collaborative optimization method described in this application, a multi-level flexible interconnected power transmission and distribution network example involving two transmission regions and two active distribution networks was constructed using MATLAB software. The transmission side adopts an IEEE 39-node system and is divided into two regions, A and B, interconnected via a flexible DC channel between nodes 16 and 17. The distribution side adopts two IEEE 33-node systems, connected to regions A and B respectively via main grid nodes 39 and 9. The two distribution networks are flexibly interconnected via a Standard Operating Procedure (SOP). The optimization period is set to 24 hours, and the system topology is as follows: Figure 2 As shown.

[0069] The transmission side employs a linear AC power flow model, while the distribution side uses a DistFlow model and a second-order cone relaxation method. Flexible DC constraints are handled within the joint optimization model on the transmission side, and SOP power coupling constraints are handled within the joint optimization model on the distribution side. The transmission and distribution sides are coordinated through ADMM iteratively using the boundary power at the transmission and distribution interfaces.

[0070] The results show that the original residual and dual residual of ADMM decrease steadily with the iteration process, reaching (10) -4 Preset convergence requirements of magnitude, such as Figure 3As shown, the feasibility of the distributed coordinated solution method is verified. During typical daily operation, the flexible DC transmission power between regions A and B can be dynamically adjusted according to the system operating status, ranging from approximately 50 to 360 MW throughout the day. Figure 4 As shown, the power of the main grid A-distribution network A interface is approximately 1.9 to 3.6 MW, and the power of the main grid B-distribution network B interface is approximately 1.3 to 2.9 MW, which can be dynamically adjusted according to changes in the net load of the distribution network.

[0071] The direction of SOP transmission between the two distribution networks changes with the differences in load and photovoltaic output, realizing bidirectional active power mutual assistance at different times. Its 24-hour transmission power is as follows: Figure 5 As shown, after the SOP is put into operation, the lowest node voltage of the dual distribution network increases from 0.9198 pu to 0.9265 pu; when the main grid flexible interconnection and the SOP are put into operation simultaneously, the lowest node voltage further increases to 0.9272 pu, indicating that the flexible interconnection of the distribution layer can improve the voltage level of weak nodes.

[0072] Further, a regional power imbalance scenario was set up, resulting in a heavy load in region A and a relatively surplus in region B within 17–21 hours. After the flexible DC transmission system was put into operation, the total inter-regional power exchange reached 5201.47 MWh, with a peak exchange power of 359.77 MW, reducing the generation cost of conventional units by approximately 0.711%. Simultaneously, the number of lines with a daily load factor of at least 90% decreased from 109 to 98, a reduction of approximately 10.09%. The results demonstrate that the multi-level flexible interconnection and collaborative optimization method adopted in this application can achieve coordinated operation of flexible resources at different levels through inter-regional power mutual assistance at the transmission layer and flexible power regulation at the distribution layer, thereby improving the economic operation of the system and the network's operational status.

[0073] Therefore, this application adopts the aforementioned distributed collaborative optimization method and system for multi-level flexible interconnected transmission and distribution networks. By embedding flexible DC interconnection constraints into the joint optimization model of the flexible interconnected transmission network and SOP flexible interconnection constraints into the joint optimization model of the flexible interconnected distribution network based on the physical connection relationship of the flexible interconnection equipment, only the transmission and distribution interfaces are used as the distributed coordination boundary between the transmission side and the distribution side. This avoids setting all cross-network connections as external coordination boundaries, reducing boundary variables, information interaction, and multi-level iterative relationships caused by over-decomposition, while maintaining the scalability of the distributed optimization model. Furthermore, by simultaneously solving the flexible DC transmission power and the power flow state of the connected transmission network in the same transmission side model, and simultaneously solving the SOP port power and the power flow state of the connected distribution network in the same distribution side model, this approach ensures the optimization of the distributed network's scalability. The solution aims to coordinate and satisfy equipment power balance, capacity constraints, and network operation constraints in the same optimization process. It achieves consistent coordination of active and reactive power boundary coupling variables at transmission and distribution interfaces using the alternating direction multiplier method. Through limited transmission and distribution boundary information exchange, it ensures consistent active and reactive power exchange at the same transmission and distribution interface on both the transmission and distribution sides, while satisfying network operation constraints on both sides. Furthermore, by establishing a unified multi-time-period joint optimization framework, it comprehensively coordinates conventional generating units, wind power, and flexible DC transmission within each transmission network, distributed photovoltaic and SOPs within each distribution network, and active and reactive power exchange at transmission and distribution interfaces. This approach satisfies the operation constraints of transmission networks, distribution networks, and flexible interconnection equipment while also considering operational requirements such as power generation economics, renewable energy consumption, cross-network power exchange, distribution network losses, and node voltage.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of this application, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of this application.

Claims

1. A distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network, characterized in that, Includes the following steps: S1: Obtain network data and determine the joint solution range for the transmission and distribution sides based on the physical connection relationship of the flexible interconnection equipment; S2: Establish a joint optimization model for flexible interconnected transmission networks. With the goal of minimizing the sum of the generation cost of conventional units and the cost of wind curtailment penalty within the transmission-side set, incorporate the operational constraints of flexible DC interconnection as internal constraints of the transmission side into the joint optimization model of the transmission side. Jointly determine the output of conventional units, wind power output, AC network power flow, flexible DC transmission power, and the local active and reactive boundary coupling variables of each transmission and distribution interface on the transmission side. S3: Establish a joint optimization model for flexible interconnected distribution networks, with the goal of minimizing the sum of line losses and node voltage deviations within the distribution-side set. The SOP flexible interconnection constraint is incorporated into the joint optimization model as an internal constraint of the distribution side. Jointly determine the distributed photovoltaic output, distribution network power flow, node voltage, line current, active and reactive power of the SOP port, and the local active and reactive power boundary coupling variables of the distribution side at the transmission and distribution interface. S4: Construct consistency constraints for the local boundary coupling variables established by the transmission and distribution sides for the same transmission and distribution interface, and perform distributed coordination based on the alternating direction multiplier method. Construct an augmented Lagrangian function and decompose it into a transmission side subproblem and a distribution side subproblem for alternating iterative solution. During the iteration process, update the Lagrangian multipliers and the penalty factor of the alternating direction multiplier method until the original residual and the dual residual satisfy the convergence condition. S5: After meeting the convergence conditions, output the operating results of conventional units and wind power in each transmission network, flexible DC transmission power, active and reactive power exchange at each transmission and distribution interface, as well as photovoltaic output, node voltage, line power flow and SOP port adjustment in each distribution network, and generate corresponding dispatch instructions or operation evaluation indicators according to application requirements.

2. The distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to claim 1, characterized in that, The determination of the joint solution scope for the transmission and distribution sides based on the physical connection relationship of the flexible interconnection equipment specifically includes: AC transmission networks that are directly connected via flexible DC and whose transmission power needs to be jointly determined in the optimization process will be included in the same transmission-side joint optimization scope; Distribution networks that are directly connected via SOPs and whose port power needs to be jointly determined in the optimization process will be included in the same distribution side joint optimization scope. The transmission side and the distribution side establish a distributed coordination relationship only through the boundary coupling variables corresponding to each transmission and distribution interface.

3. The distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to claim 2, characterized in that, In S1, for the same group of physical exchange power at transmission and distribution interface m in time period t, local active power boundary coupling variables and local reactive power boundary coupling variables are set on the transmission side and the distribution side respectively, with the same positive power direction and power reference used on both sides; the boundary coupling variable of the m-th transmission and distribution interface in time period t is expressed as: ； ； Where z is a two-dimensional boundary coupling variable arranged in [P,Q]; For the transmission network side boundary coupling variables, For the boundary coupling variables of the distribution network side, P and Q represent the active and reactive power of the interface, respectively; m is the index of the transmission and distribution interface; t=1,2,…,T is the time period index; the superscripts TN and DN represent the transmission network side and the distribution network side, respectively. , These are the sets of transmission networks and distribution networks participating in this joint optimization, respectively, where n and d are the indices of the transmission network and distribution network, respectively. , These are the transmission and distribution interface sets belonging to transmission network n and distribution network d, respectively.

4. The distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to claim 3, characterized in that, The objective, aiming to minimize the sum of the generation cost of conventional units within the transmission-side cluster and the cost of wind curtailment penalty, is expressed as: ； in, The original objective function on the transmission side is... , These are the costs of generating electricity from conventional units and the cost of wind curtailment penalties, respectively. The power generation cost of a conventional generating unit is expressed as follows: ； in, Let g be the unit active power generation cost. For the active power output of unit g. The duration of a single time period; The cost of wind curtailment penalties is expressed as follows: ； in, Let w be the penalty coefficient for the unit amount of wind curtailment of the wind turbine. , These are available wind power and actual output, respectively.

5. The distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to claim 4, characterized in that, Operational constraints for flexible DC interconnects include: Flexible DC transmission generates active power injection at the endpoints of the connected power grid, as shown below: ； The flexible DC transmission power satisfies the operating boundary, as expressed as: ； in, It is a flexible DC collection, For flexible DC collection index, Active power for flexible DC transmission; For endpoint position and injection direction coefficients, the receiving end is set to 1, the sending end is set to -1, and non-access nodes are set to 0. Flexible DC The transmitted active power during time period t, This is the corresponding lower power limit. This represents the corresponding power limit.

6. The distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to claim 1, characterized in that, The objective, which aims to minimize the sum of line losses and node voltage deviations within the distribution-side aggregation, is expressed as: ； in, The original objective function on the distribution side, , These are the line loss target and the node voltage deviation target, respectively. The line loss target is: ； in, For loss weight, For line resistance, The square of the line current amplitude. Let d be the set of lines in the distribution network. The target for node voltage deviation is: ； in, As the voltage deviation weight, The square of the node voltage magnitude. The square of the reference voltage amplitude, For distribution network The set of nodes.

7. The distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to claim 6, characterized in that, In step S3, the SOP flexible interconnection constraints include SOP active power constraints and SOP capacity constraints. For a two-port SOP connecting distribution network port A and port B, the active power constraint of SOP is expressed as: ； in, , These represent the active power injected by SOP into distribution network port A and distribution network port B during time period t, respectively. Positive values ​​indicate that the power is injected into the distribution network, while negative values ​​indicate that the power is absorbed from the distribution network. , These represent the non-negative active power losses at the two ports, respectively. and These are the loss coefficients for port A and port B, respectively. and Reactive power injection is performed on ports A and B, respectively. The active and reactive power of each SOP port are both limited by the port's rated capacity: ； in, The rated apparent capacity of the SOP; For a general form involving multiple ports, the SOP active power balance and port losses are expressed as follows: ； in, Index for SOP devices; For device s, the set of ports; For port indexing; , Ports The active and reactive power injected into the distribution network during time period t; For port Active power loss; Port loss coefficient; The active and reactive power of each SOP port are both limited by the port's rated capacity: 。 8. The distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to claim 7, characterized in that, For the local boundary coupling variables established by the transmission and distribution sides for the same transmission and distribution interface, a consistency constraint is constructed, which is expressed as: ； in, , Let the decision variable vectors be those for the transmission and distribution sides. , The feasible regions are composed of the operational constraints on the transmission side and the distribution side, respectively. This refers to the set of all transmission and distribution interfaces connected to the transmission network n and the distribution network d. Let m be the overall objective function for transmission and distribution coordination; for the same transmission and distribution interface m and the same time period t, For transmission-side local boundary coupling variables, These are local boundary coupling variables on the distribution side. Both are composed of active and reactive power exchange quantities at the interface, and the same positive power direction and power reference are specified. Since the variables on both sides correspond to the exchange power of the same physical interface, the consistency constraint of the transmission and distribution interface is satisfied. This ensures that the descriptions of the operating status of the transmission and distribution interfaces are consistent between the transmission side and the distribution side. The augmented Lagrangian function is constructed as follows: ； in, Let k be the Lagrange multiplier vector of the kth round. >0 is the penalty factor, and ||·|2 is the second-order norm; in the k-th iteration, after fixing the interface variables of the distribution side in the previous round, the subproblem of the transmission side is expressed as: ； in, For the k-th iteration, the local boundary coupling variable on the distribution side; After fixing the current-round interface variables on the transmission side, the distribution side subproblem is represented as: ； in, For the (k+1)th iteration, the local boundary coupling variable on the transmission side is denoted as .

9. The distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network according to claim 8, characterized in that, During the iteration process, the Lagrange multipliers and the penalty factor for the alternating direction multiplier method are updated until the original residual and the dual residual satisfy the convergence condition, specifically including: Initialize the transmission and distribution interface boundary coupling variables, Lagrange multipliers, penalty factors and their upper and lower limits, residual balance parameters, convergence threshold, and maximum number of iterations; in the k-th round, solve the transmission side sub-problem and send the results. Then solve the distribution side problem and return. The original residuals are expressed as: ； in, The maximum original residual for all transmission and distribution interfaces, ||·|| ∞ It is an infinite norm; The dual residuals corresponding to two consecutive rounds of changes in the interface variable are: ； in, The maximum dual residual for all transmission and distribution interfaces; Lagrange multiplier vectors are updated as follows: ； in, , These are the Lagrange multiplier vectors before and after the update, respectively; The iteration ends when both the original residual and the dual residual simultaneously satisfy the threshold. ； in, , These are the thresholds for the original residual and the dual residual, respectively; if convergence fails and the maximum number of iterations has not been reached, the problem returns to the transmission side subproblem. The penalty factor for the next round is adaptively adjusted based on the relative magnitudes of the original residual and the dual residual: ； Where μ is the residual balance threshold; , These are the magnification / reduction factors; , These are the upper and lower limits of the penalty factor; the penalty factor is increased when the original residual is too large, and decreased when the dual residual is too large; otherwise, they remain unchanged. The updated... Used for the next iteration.

10. A distributed collaborative optimization system for a multi-level flexible interconnected power transmission and distribution network, employing the distributed collaborative optimization method for a multi-level flexible interconnected power transmission and distribution network as described in any one of claims 1-9, characterized in that, include: The module includes a data acquisition and joint solution range determination module, a transmission-side joint optimization module, a distribution-side joint optimization module, a transmission-distribution boundary distributed coordination module, and an optimization result output module. The data acquisition and joint solution scope determination module is used to acquire network topology, load and new energy forecast data of each transmission network and distribution network, as well as the access location and operating parameters of flexible DC, transmission and distribution interfaces and SOPs; determine the joint solution scope of the transmission side and distribution side based on the physical connection relationship of flexible interconnection equipment, and establish the correspondence between the transmission and distribution interfaces and their respective transmission and distribution networks; The transmission-side joint optimization module is used to establish a joint optimization model for the flexible interconnected transmission network that participates in collaborative optimization. It jointly determines the output of conventional units, wind power output, AC network power flow, flexible DC transmission power, and local active and reactive boundary coupling variables of the transmission and distribution interface. Among them, the flexible DC interconnection constraint is directly incorporated into the joint optimization model as an internal constraint of the transmission side. The distribution-side joint optimization module is used to establish a joint optimization model for the flexible interconnected distribution network that participates in collaborative optimization. It jointly determines the distributed photovoltaic output, distribution network power flow, node voltage, line current, active and reactive power at the SOP port, and local active and reactive power boundary coupling variables at the transmission and distribution interface. Among them, the SOP flexible interconnection constraint is directly incorporated into the joint optimization model as an internal constraint of the distribution side. The transmission and distribution boundary distributed coordination module is used to construct consistency constraints for the local boundary coupling variables established by the transmission side and the distribution side for the same transmission and distribution interface, and to perform distributed coordination based on the alternating direction multiplier method. In each iteration, the module passes the corresponding boundary variables, Lagrange multipliers and penalty factors to the transmission side joint optimization module and the distribution side joint optimization module, obtains the active and reactive power of the interface obtained from the solutions on both sides, calculates the original residual and dual residual, updates the Lagrange multipliers, and adjusts the penalty factor according to the preset rules until the convergence condition is met. The optimization result output module is used to output the operating results of conventional units and wind power in each transmission network, flexible DC transmission power, active and reactive power exchange at each transmission and distribution interface, photovoltaic output, node voltage, line power flow and SOP port adjustment of each distribution network after the convergence conditions are met, and to generate corresponding dispatch instructions or operation evaluation indicators according to application requirements.

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