Optimized operation method and device for power distribution network based on cloud edge collaboration

Abstract

The invention relates to a power distribution network optimization operation method and device based on cloud-edge collaboration, and the method comprises the steps: taking cloud-edge collaboration as a core, and constructing a regulation and control architecture of the cloud-edge collaboration of a power distribution network; the method comprises the following steps: establishing a cloud-edge collaborative power distribution network optimization operation model according to operation data of a power distribution network based on a power distribution network cloud-edge collaborative regulation and control framework; and constructing the cloud-side collaborative power distribution network optimization operation model into a cloud management and control platform main problem model and a side-end cluster sub-problem model so as to optimize and output a final optimization result of the power distribution network by using the cloud management and control platform main problem model and the side-end cluster sub-problem model. According to the embodiment of the invention, cloud-side cooperation is taken as a core, a brand new power distribution network operation control architecture is constructed, the operation control capability of the power distribution network is effectively improved through deep integration of'operation data + artificial intelligence 'and a power distribution system operation control technology, and an optimal strategy is provided for safe and stable operation of the power distribution network under a high-proportion distributed power supply access condition.

Description

Technical Field

[0001] This application relates to the technical field of power system operation, and particularly relates to a method and device for optimizing the operation of a distribution network based on cloud-edge collaboration. Background Art

[0002] The large-scale and high-proportion access of distributed power sources has brought significant challenges to the stable operation of the distribution network. Distributed power sources are intermittent and uncertain, resulting in a substantial increase in the volatility of the distribution network operation. The access of distributed power sources makes the original unidirectional power flow more complex and changeable. This not only impacts the economy and security of the distribution network but also makes its optimization and control methods face higher complexity.

[0003] Meanwhile, with the rapid development of communication and network technologies, a new production method centered on digitization, with data as the key production factor and digital technology as the driving force, has emerged rapidly. Human society is accelerating into the digital economy era. To adapt to this development trend, the distribution network is actively integrating advanced informatization and digital technologies, continuously improving its digital, networked, and intelligent levels. Breakthrough achievements of various digital technologies in different fields have brought new development opportunities to the distribution system and provided new ideas for solving the operation control problems brought by the high-proportion access of distributed power sources. Among them, edge computing technology and its derived cloud-edge collaborative operation mode highly coincide with the operation control architecture of the distribution network under the condition of high-proportion access of distributed power sources and can be used as a flexible and efficient solution to help the distribution network achieve intelligent operation and control.

[0004] In related technologies, for a distribution network with high-proportion access of distributed power sources, traditionally, a centralized control mode is usually adopted to achieve global optimization by uniformly dispatching controllable resources; the decentralized control method divides the distribution network reasonably and uses the boundary information interaction between adjacent regions to achieve overall optimization, reducing some communication requirements; the local control method can regulate local resources only relying on local information, with the advantages of fast response speed, low investment cost, and small communication data volume.

[0005] However, the modes in related technologies need to collect and process a large amount of measurement data, bringing high communication and computing costs; frequent communication is still required between regional controllers, and multiple iterations are needed to obtain the final optimization result; due to the inability to fully coordinate and utilize the controllable resources in the entire distribution system, its adjustment ability is relatively limited and urgently needs to be improved. Summary of the Invention

[0006] This application provides a method and device for optimizing the operation of a distribution network based on cloud-edge collaboration, aiming to solve problems in related technologies such as the need to collect and process a large amount of measurement data, which brings high communication and computing costs; frequent communication is still required between regional controllers, and multiple iterations are needed to obtain the final optimization result; due to the inability to fully coordinate and utilize controllable resources in the entire distribution system, its adjustment ability is relatively limited, etc.

[0007] In the first aspect of the embodiments of this application, a method for optimizing the operation of a distribution network based on cloud-edge collaboration is provided, which is applied to the model construction stage and includes the following steps: taking cloud-edge collaboration as the core, constructing a control and regulation architecture for cloud-edge collaboration of the distribution network; based on the control and regulation architecture for cloud-edge collaboration of the distribution network, establishing an optimized operation model for the cloud-edge collaborative distribution network according to the operation data of the distribution network; constructing the optimized operation model for the cloud-edge collaborative distribution network into a main problem model of the cloud control platform and a sub-problem model of the edge cluster to optimize and output the final optimization result of the distribution network by using the main problem model of the cloud control platform and the sub-problem model of the edge cluster.

[0008] Optionally, in an embodiment of this application, after constructing the optimized operation model for the cloud-edge collaborative distribution network into a main problem model of the cloud control platform and a sub-problem model of the edge cluster, it further includes: when the scheduling cost and system network loss of the main problem model of the cloud control platform meet the first preset minimization condition, determining the constraint conditions of the main problem model of the cloud control platform according to the first power flow constraint and the first safe operation constraint; when the internal network loss of the edge cluster of the sub-problem model of the edge cluster meets the second preset minimization condition, determining the constraint conditions of the sub-problem model of the edge cluster according to the second power flow constraint, the second safe operation constraint and the boundary condition constraint.

[0009] Optionally, in an embodiment of this application, the calculation formula of the first power flow constraint is:

[0010]

[0011] where P ij , Q ij respectively represent the active and reactive power flows of branch ij, V i is the voltage amplitude of node i, G ij , B ij respectively represent the equivalent conductance and susceptance of branch ij, θ ij is the phase angle difference between the head and tail of branch ij, the set π(i) represents the set of nodes directly connected to node i, Q Gi is the reactive power injection of distributed photovoltaic and other generating units at node i, and Q Di is the reactive power load at node i;

[0012] The calculation formula for the first safe operation constraint is as follows:

[0013]

[0014] Among them, P Gi,min , P Gi,max respectively represent the minimum and maximum active power outputs of distributed photovoltaic and other generating units at node i, Q Gi,min , Q Gi,max respectively represent the minimum and maximum reactive power outputs of distributed photovoltaic and other generating units at node i, V i,min , V i,max respectively represent the minimum and maximum voltage amplitudes of node i, S ij,max respectively represent the transmission capacity of branch ij.

[0015] Optionally, in an embodiment of the present application, the calculation formula for the second power flow constraint is as follows:

[0016]

[0017] Among them, I ij is the current amplitude of branch ij, r ij , x ij respectively represent the resistance and reactance of branch ij, the set u(i) is the parent node of node i in the radial power grid, and the set v(i) is the child node of node i in the radial power grid;

[0018] The calculation formula for the second safety constraint is as follows:

[0019]

[0020] Among them, I ij,max is the upper limit of the current amplitude of branch ij, and the subscript d represents the variables and parameters inside the cluster;

[0021] The calculation formula for the boundary condition constraint is as follows:

[0022]

[0023] Among them, respectively represent the equivalent active and reactive power loads of the kth cluster in the cloud control platform, respectively represent the active and reactive power injections from the cloud control platform to the root node of the kth cluster, respectively represent the node voltage amplitude and phase angle of the node connecting the kth cluster in the cloud control platform, respectively represent the voltage amplitude and phase angle of the kth root node.

[0024] In the second aspect of the embodiments of the present application, an optimized operation method for a distribution network based on cloud-edge collaboration is provided, which is applied to the model application stage and includes the following steps: obtaining at least one of the voltage, current, and power status information of each node in the distribution network and the output information of distributed power sources; inputting the at least one status information and the output information into a pre-constructed optimized operation model of the distribution network, and based on the optimized operation model of the distribution network, interacting the coupling information between the edge-end cluster and the cloud control platform to generate an interaction result, and optimizing the operation of the distribution network according to the interaction result to generate the final optimized result of the distribution network, where the optimized operation model of the distribution network is constructed from the operation data of the distribution network.

[0025] Optionally, in an embodiment of the present application, the optimizing the distribution network according to the optimized operation model of the distribution network to generate the final optimized result of the distribution network includes: in the case of cloud-edge collaborative optimization scheduling, unifying the boundary coupling constraints into the objective function to construct the augmented Lagrangian function corresponding to the first cluster and the second cluster; determining the consistency global variables of the first cluster and the second cluster according to the fixed reference values iterated by the first cluster and the second cluster, and updating the consistency global variables and the augmented Lagrangian function to generate the updated consistency global variables and the updated Lagrange multiplier vector; based on the updated consistency global variables and the updated Lagrange multiplier vector, calculating the primal residual and the dual residual of the cluster optimization scheduling, and determining whether the primal residual and the dual residual satisfy a preset convergence condition; if the primal residual and the dual residual satisfy the preset convergence condition, stopping the iteration of the solution of the optimized operation model of the distribution network and generating the final optimized result of the distribution network.

[0026] Optionally, in an embodiment of the present application, the calculation formula of the augmented Lagrangian function corresponding to the first cluster and the second cluster is:

[0027]

[0028] where k is the number of iterations, and are the augmented Lagrange multiplier vectors corresponding to clusters and respectively, ρ is the penalty parameter;

[0029] The calculation formula of the consistency global variables of the first cluster and the second cluster is:

[0030]

[0031] where, and They are the boundary coupling variables during the internal solution of the two clusters in the k-th iteration respectively. and They are the boundary coupling variables for the interaction with the cloud control platform respectively.

[0032] The third aspect embodiment of this application provides a distribution network optimal operation device based on cloud-edge collaboration, which is applied to the model construction stage, including: a construction module, used to build a regulation architecture for cloud-edge collaboration of the distribution network with cloud-edge collaboration as the core; an establishment module, used to establish an optimal operation model for cloud-edge collaboration of the distribution network based on the regulation architecture for cloud-edge collaboration of the distribution network according to the operation data of the distribution network; an optimization module, used to construct the optimal operation model for cloud-edge collaboration of the distribution network into a main problem model of the cloud control platform and a sub-problem model of the edge cluster, so as to optimize and output the final optimization result of the distribution network by using the main problem model of the cloud control platform and the sub-problem model of the edge cluster.

[0033] Optionally, in an embodiment of this application, it further includes: a first determination module, used to, after constructing the optimal operation model for cloud-edge collaboration of the distribution network into a main problem model of the cloud control platform and a sub-problem model of the edge cluster, determine the constraint conditions of the main problem model of the cloud control platform according to the first power flow constraint and the first safe operation constraint when the scheduling cost and system power loss of the main problem model of the cloud control platform meet the first preset minimization condition; a second determination module, used to determine the constraint conditions of the sub-problem model of the edge cluster according to the second power flow constraint, the second safe operation constraint and the boundary condition constraint when the internal power loss of the edge cluster in the sub-problem model of the edge cluster meets the second preset minimization condition.

[0034] Optionally, in an embodiment of this application, the calculation formula of the first power flow constraint is:

[0035]

[0036] where P ij , Q ij respectively represent the active and reactive power flows of branch ij, V i is the voltage amplitude of node i, G ij , B ij respectively represent the equivalent conductance and susceptance of the branch ij, θ ij is the phase angle difference between the head and tail of the branch ij, the set π(i) represents the set of nodes directly connected to the node i, Q Gi is the reactive power injection of distributed photovoltaic and other generator sets at the node i, Q Di is the reactive power load at the node i;

[0037] The calculation formula of the first safe operation constraint is:

[0038]

[0039] Among them, P Gi,min , P Gi,max respectively represent the minimum and maximum active power outputs of distributed photovoltaic and other generating units at the node i, Q Gi,min , Q Gi,max respectively represent the minimum and maximum reactive power outputs of distributed photovoltaic and other generating units at the node i, V i,min , V i,max respectively represent the minimum and maximum voltage amplitudes of the node i, S ij,max respectively represent the transmission capacity of the branch ij.

[0040] Optionally, in an embodiment of the present application, the calculation formula of the second power flow constraint is:

[0041]

[0042] Among them, I ij is the current amplitude of the branch ij, r ij , x ij respectively represent the resistance and reactance of the branch ij, the set u(i) is the parent node of the node i in the radial power grid, and the set v(i) is the child node of the node i in the radial power grid;

[0043] The calculation formula of the second security constraint is:

[0044]

[0045] Among them, I ij,max is the upper limit of the current amplitude of the branch ij, and the subscript d represents the variables and parameters inside the cluster;

[0046] The calculation formula of the boundary condition constraint is:

[0047]

[0048] Among them, respectively represent the equivalent active and reactive power loads of the kth cluster in the cloud control platform, respectively represent the active and reactive power injections from the cloud control platform to the root node of the kth cluster, respectively represent the node voltage amplitude and phase angle of the node connecting the kth cluster in the cloud control platform, respectively represent the voltage amplitude and phase angle of the kth root node.

[0049] In the fourth aspect of the embodiments of the present application, a device for optimizing the operation of a distribution network based on cloud-edge collaboration is provided, which is applied to the model application stage and includes: an acquisition module, configured to acquire at least one of the voltage, current, and power status information of each node in the distribution network and the output information of distributed power sources; a generation module, configured to input the at least one status information and the output information into a pre-constructed distribution network optimization operation model, and generate an interaction result based on the coupling information between the edge-end cluster and the cloud control platform in the distribution network optimization operation model, and optimize the operation of the distribution network according to the interaction result to generate the final optimization result of the distribution network, where the distribution network optimization operation model is constructed from the operation data of the distribution network.

[0050] Optionally, in an embodiment of the present application, the generation module includes: a unification unit, configured to unify the boundary coupling constraints into the objective function in the case of cloud-edge collaborative optimization scheduling to construct an augmented Lagrangian function corresponding to the first cluster and the second cluster; an update unit, configured to determine the consistency global variables of the first cluster and the second cluster according to the fixed reference values iterated by the first cluster and the second cluster, and update the consistency global variables and the augmented Lagrangian function to generate updated consistency global variables and an updated Lagrange multiplier vector; a judgment unit, configured to calculate the primal residual and the dual residual of the cluster optimization scheduling based on the updated consistency global variables and the updated Lagrange multiplier vector, and judge whether the primal residual and the dual residual satisfy a preset convergence condition; a generation unit, configured to stop the iteration of the solution of the distribution network optimization operation model and generate the final optimization result of the distribution network when the primal residual and the dual residual satisfy the preset convergence condition.

[0051] Optionally, in an embodiment of the present application, the calculation formula of the augmented Lagrangian function corresponding to the first cluster and the second cluster is:

[0052]

[0053] where k is the number of iterations, and are the augmented Lagrange multiplier vectors corresponding to the clusters and respectively, and ρ is the penalty parameter;

[0054] The calculation formula of the consistency global variables of the first cluster and the second cluster is:

[0055]

[0056] where, and They are the boundary coupling variables during the internal solution of the two clusters in the k-th iteration respectively. and They are the boundary coupling variables for interaction with the cloud control platform respectively.

[0057] An embodiment of the fifth aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the method for optimizing the operation of a distribution network based on cloud-edge collaboration as described in the above embodiments.

[0058] The embodiments of the present application take cloud-edge collaboration as the core, construct a new operation control architecture for the distribution network, break through the limitations of existing methods by deeply integrating "operation data + artificial intelligence" with the operation control technology of the distribution system, effectively improve the operation control ability of the distribution network, and provide an optimal strategy for the safe and stable operation of the distribution network under the condition of high-proportion distributed power access. Thus, it solves the problems in the related technologies that the mode needs to collect and process a large amount of measurement data, bringing high communication and computing costs; frequent communication is still required between regional controllers, and multiple iterations are needed to obtain the final optimization result; due to the inability to fully coordinate and utilize the controllable resources in the entire distribution system, its regulation ability is relatively limited, etc.

[0059] The additional aspects and advantages of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0061] Figure 1 It is a flowchart of a method for optimizing the operation of a distribution network based on cloud-edge collaboration according to an embodiment of the present application applied to the model construction stage;

[0062] Figure 2 It is an architecture diagram of an optimized control of a distribution network based on cloud-edge collaboration according to an embodiment of the present application;

[0063] Figure 3 It is a flowchart of a method for optimizing the operation of a distribution network based on cloud-edge collaboration according to an embodiment of the present application applied to the model application stage;

[0064] Figure 4 It is a flowchart for solving an optimized model of a distribution network based on cloud-edge collaboration according to an embodiment of the present application;

[0065] Figure 5Schematic diagram of an optimized operation device for a distribution network based on cloud-edge collaboration according to an embodiment of the present application applied to the model construction stage;

[0066] Figure 6 Schematic diagram of an optimized operation device for a distribution network based on cloud-edge collaboration according to an embodiment of the present application applied to the model application stage;

[0067] Figure 7 Schematic diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0068] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

[0069] The optimized operation method and device for a distribution network based on cloud-edge collaboration according to an embodiment of the present application will be described below with reference to the accompanying drawings. For the problems in the related art mentioned in the above background technology, that is, the relevant technology mode needs to collect and process a large amount of measurement data, bringing relatively high communication and computing costs; frequent communication is still required between regional controllers, and multiple iterations are needed to obtain the final optimized result; due to the inability to fully coordinate and utilize the controllable resources in the entire distribution system, its adjustment ability is relatively limited. The present application provides an optimized operation method for a distribution network based on cloud-edge collaboration. In this method, first, a cloud-edge collaborative control architecture for the distribution network is constructed, and an optimized operation model is constructed based on this. The method continues the operation control concept of "layered + partitioned", integrates the information of the whole network through the cloud, comprehensively analyzes the operation state of the system, and formulates a global optimization strategy; the edge side flexibly adjusts according to the control instructions issued by the cloud and makes independent decisions in combination with local information. The proposed cloud-edge collaboration method effectively alleviates the communication pressure caused by the transmission of a large amount of information to the distribution network master station, avoids the calculation bottleneck in the centralized solution of the global optimization problem and the insufficient solution efficiency of complex control problems. At the same time, the optimization effect of this method is equivalent to that of the centralized method, but it has higher flexibility and adaptability, and can better meet the actual needs of the optimized operation of the distribution network.

[0070] Specifically, Figure 1 Flow chart of an optimized operation method for a distribution network based on cloud-edge collaboration provided by an embodiment of the present application.

[0071] As Figure 1 shown, when the optimized operation method for a distribution network based on cloud-edge collaboration is applied to the model construction stage, it includes the following steps:

[0072] In step S101, with cloud-edge collaboration as the core, a regulation architecture for cloud-edge collaboration of the distribution network is constructed.

[0073] It can be understood that the cloud-edge collaborative control architecture in the embodiments of this application can be divided into three parts, including: a cloud management and control platform, an edge cluster control layer, and a controllable resource device layer.

[0074] In the environment of the new power system, distributed resources show the characteristics of a large overall quantity, small single-point capacity, different characteristics, and spatial dispersion. If these resources directly accept centralized regulation, it will generate extremely high information access costs, and cloud computing will also face the problem of "curse of dimensionality", and it is also not conducive to user privacy protection. Therefore, based on the cloud-edge collaborative information architecture, a "resource cluster" intermediate layer is introduced between the "cloud" and the "edge". The "cluster" layer is a control logic layer. In this architecture, the edge computing device is the basic regulation unit, and the specific regulating devices are scheduled according to the instructions issued by the regional edge computing device. In this architecture, the cloud no longer directly issues control instructions to the specific regulating devices. From the information interaction level, the cloud is still responsible for core tasks such as the storage of global power grid status information, the processing of comprehensive power grid services, the training and operation of regulation models, and the issuance of decision-making instructions. However, different from the traditional centralized regulation architecture, the cloud no longer directly receives the status and action information of the acquisition and regulation devices, but is collected and processed by the edge computing device in different regions and then uploaded, resulting in a significant decrease in the number of real-time communication nodes on the cloud side; on the other hand, the regulation ability of the cloud is more flexible. It can either directly issue instructions or issue the decision-making model to the edge computing device, and the edge computing device directly controls the adjustable devices. Therefore, in the distribution network optimization problem, the edge computing side is given the ability of in-situ autonomy, that is, without receiving any instructions from the cloud, it uses the pre-deployed decision-making model and collects the power grid status information within its jurisdiction to make decisions.

[0075] The proposed cloud-edge collaborative control architecture is shown in the accompanying drawings in detail, and the functions of each layer are as follows:

[0076] (1) The cloud management and control platform located in the cloud is the decision-making center of the entire framework. The cloud management and control platform can generate cloud resource clusters through online dynamic clustering; through collecting the aggregation models of each cluster and carrying out information decision-making interactions with power grid institutions such as the regulation center and the trading center, it coordinates the regulation capabilities of the cluster resources and the directly regulated resources within the power grid; through cloud instruction decomposition, it decomposes the scheduling instructions issued by the power grid and issues them to each resource cluster.

[0077] (2) The "edge" level composed of the control logic layers of each resource cluster plays a connecting role in the entire control framework, responsible for online aggregating various resources within the cluster, obtaining the aggregation model of the cluster and uploading it to the cloud management and control platform, and at the same time online responding to the regulation instructions issued by the cloud management and control platform and decomposing them to various distributed resources within the group.

[0078] (3) The controllable resource device layer is the bottom layer of the entire control framework, responsible for establishing the regulation ability models of various distributed resources, collecting, sensing, and communicating real-time operation information with the upper layer, and automatically receiving and responding to the regulation instructions issued by the "edge" level.

[0079] During the actual execution process, such as Figure 2 shown, the embodiment of the present application takes cloud-edge collaboration as the core, constructs a regulation architecture for cloud-edge collaboration in the distribution network, fully considers the influence of different computing resource distributions and computing characteristics between the cloud and the edge on the operation optimization calculation granularity and response speed of the distribution network, constructs a cloud-edge collaborative control architecture for the distribution network, effectively coordinates the computing resources at different levels of the distribution system, realizes the local formulation of adjustable resource control strategies, and provides a faster, more flexible, and intelligent control means for the distribution network. The embodiment of the present application proposes a cloud-edge collaborative control architecture, combines the "layered + partitioned" operation control method, and realizes cloud-edge collaborative optimization. It reduces the communication cost and computing pressure of the original distribution network and improves the computing efficiency.

[0080] In step S102, based on the regulation architecture for cloud-edge collaboration in the distribution network, an optimized operation model of the cloud-edge collaborative distribution network is established according to the operation data of the distribution network.

[0081] During the actual execution process, the embodiment of the present application can establish an optimized operation model of the cloud-edge collaborative distribution network based on the regulation architecture for cloud-edge collaboration in the distribution network, provide support for subsequently constructing the original model into the main problem model of the cloud management and control platform and the sub-problem model of the edge cluster, and then only interact the coupling information between the edge cluster and the cloud management and control platform, reducing the communication volume and complexity.

[0082] In step S103, the optimized operation model of the cloud-edge collaborative distribution network is constructed into the main problem model of the cloud management and control platform and the sub-problem model of the edge cluster, so as to optimize and output the final optimized result of the distribution network by using the main problem model of the cloud management and control platform and the sub-problem model of the edge cluster.

[0083] It can be understood that with the development of the Internet of Things cloud-edge technology, the proposed cloud-edge collaboration method adopts the method of communication between the cloud management and control platform and multiple cluster edge points. Therefore, the optimized operation model of the cloud-edge collaborative distribution network is constructed into the main problem model of the cloud management and control platform and the sub-problem model of the edge cluster, and the final optimized result of the distribution network is optimized and output by using the main problem model of the cloud management and control platform and the sub-problem model of the edge cluster, reducing the computing tasks and communication pressure of the edge cluster to the lowest level, and realizing that the edge cluster only needs to communicate with the cloud management and control platform without directly iterating with other edge clusters.

[0084] Optionally, in an embodiment of the present application, after constructing the cloud-edge collaborative distribution network optimal operation model into the main problem model of the cloud control platform and the sub-problem model of the edge cluster, it further includes: when the scheduling cost and system network loss of the main problem model of the cloud control platform meet the first preset minimization condition, determining the constraint conditions of the main problem model of the cloud control platform according to the first power flow constraint and the first safe operation constraint; when the internal network loss of the edge cluster in the sub-problem model of the edge cluster meets the second preset minimization condition, determining the constraint conditions of the sub-problem model of the edge cluster according to the second power flow constraint, the second safe operation constraint and the boundary condition constraint.

[0085] It can be understood that the first preset minimization condition in the embodiment of the present application is to minimize the scheduling cost and system network loss of the main problem model of the cloud control platform, and the first power flow constraint and the first safe operation constraint are the constraint conditions of the main problem model of the cloud control platform; the second power flow constraint and the second safe operation constraint in the embodiment of the present application are the constraint conditions of the sub-problem model of the edge cluster.

[0086] Specifically, the embodiment of the present application can optimize the main problem model of the cloud control platform, aiming at minimizing the scheduling cost and system network loss, and determining the constraint conditions of the main problem model of the cloud control platform according to the power flow constraint and safe operation constraint of the main problem model of the cloud control platform. When optimizing the sub-problem model of the edge cluster, the embodiment of the present application needs to effectively track the active power control instructions sent from the cloud, and aiming at minimizing the internal network loss of the cluster, determining the constraint conditions of the sub-problem model of the edge cluster according to the power flow constraint, safe operation constraint and boundary condition constraint of the sub-problem model of the edge cluster.

[0087] The edge-side devices in the embodiment of the present application can make autonomous decisions by combining local information, have the ability of rapid response, and are more flexible than traditional centralized methods in dealing with emergency working conditions (such as voltage deviation, sudden increase in load), significantly improving the adaptability of the distribution network to the access of high-proportion distributed power sources. At the same time, the autonomous decision-making ability on the edge side reduces the dependence on high-performance communication networks and reduces the demand for centralized computing devices, thus significantly saving the investment cost and operation cost of the system.

[0088] (1) The main problem model of the cloud control platform, the optimization model aims at minimizing the scheduling cost and system network loss, and the specific expression is as follows:

[0089]

[0090] Among them, C t respectively represent the distribution network network loss cost and the active and reactive power resource optimal scheduling cost.

[0091] (2) For the constraint conditions of the main problem model of the cloud control platform, including power flow constraints and safe operation constraints, the power flow equation is expressed in polar coordinates.

[0092] Among them, in an embodiment of the present application, the calculation formula of the first power flow constraint is:

[0093]

[0094] Among them, constraint equations (2) and (3) are the active and reactive power flow equations in polar coordinates, and constraint equations (4) and (5) are the active and reactive power balances of each node. P ij , Q ij respectively represent the active and reactive power flows of branch ij, V i is the voltage amplitude of node i, G ij , B ij respectively represent the equivalent conductance and susceptance of branch ij, θ ij is the phase angle difference between the head and end of branch ij, the set π(i) represents the set of nodes directly connected to node i, Q Gi is the reactive power injection of distributed photovoltaic and other generating units at node i, Q Di is the reactive power load at node i;

[0095] The calculation formula of the first safe operation constraint is:

[0096]

[0097] Among them, P Gi,min , P Gi,max respectively represent the minimum and maximum active power outputs of distributed photovoltaic and other generating units at node i, Q Gi,min , Q Gi,max respectively represent the minimum and maximum reactive power outputs of distributed photovoltaic and other generating units at node i, V i,min , V i,max respectively represent the minimum and maximum voltage amplitudes of node i, S ij,max respectively represent the transmission capacities of branch ij. The above formulas are the active power output limit, reactive power output limit, network node voltage amplitude limit, and transmission capacity limit of each line for distributed photovoltaic and other generating units.

[0098] (3) Edge cluster sub-problem model. The optimization model needs to effectively track the active power control instructions issued by the cloud while minimizing the internal network loss of the cluster. The specific expression is as follows:

[0099]

[0100] Among them, F is the total objective function value; f 1 are the objective function λQ The result after normalization; S 1 Are respectively the reference values of the objective function; w 1 、w 2 Are respectively the weights of the objective function, and satisfy w 1 +w 2 = 1, and are all positive numbers; Is the Lagrangian function of the objective function including the coupling constraint between the cloud control platform and the cluster boundary.

[0101] (4) For the constraint conditions of the edge-side cluster sub-problem model, including power flow constraints, safe operation constraints, and boundary condition constraints, the Distflow equation is used to describe them.

[0102] Among them, in an embodiment of the present application, the calculation formula of the second power flow constraint is:

[0103]

[0104]

[0105] Among them, I ij Is the current amplitude of branch ij, r ij , x ij Respectively represent the resistance and reactance of branch ij, the set u(i) is the parent node of node i in the radial power grid, and the set v(i) is the child node of node i in the radial power grid;

[0106] The calculation formula of the second safety constraint is:

[0107]

[0108] Among them, I ij,max Is the upper limit of the current amplitude of branch ij, and the subscript d represents the variables and parameters inside the cluster; the above constraints are respectively the active power output limit, reactive power output limit, network node voltage amplitude limit, and transmission capacity limit of each line of distributed photovoltaic and other generating units.

[0109] The calculation formula of the boundary condition constraint is:

[0110]

[0111] Among them, Respectively represent the equivalent active and reactive power loads of the kth cluster in the cloud control platform, Respectively represent the active and reactive power injections from the cloud control platform to the root node of the kth cluster, Respectively represent the voltage amplitude and phase angle of the node connecting the kth cluster in the cloud control platform, Respectively represent the voltage amplitude and phase angle of the kth root node.

[0112] As Figure 3 shown, a method for optimizing the operation of a distribution network based on cloud-edge collaboration, which is applied to the model application stage, includes the following steps:

[0113] In step S301, at least one of the voltage, current, and power status information of each node in the distribution network and the output information of distributed power sources is obtained.

[0114] It can be understood that the output information in the embodiments of the present application may include the active power output limit, reactive power output limit, etc. of distributed photovoltaic and other generator sets.

[0115] Among them, the embodiments of the present application can obtain the status information such as the voltage, current, and power of each node in the distribution network and the output information of distributed power sources, so as to provide support for reducing the dependence on high-performance communication networks and at the same time reducing the demand for centralized computing devices in the subsequent process, thereby significantly saving the investment cost and operation cost of the system.

[0116] In step S302, at least one of the status information and the output information is input into a pre-constructed distribution network optimization operation model, and the coupling information between the edge cluster and the cloud control platform is interacted based on the distribution network optimization operation model to generate an interaction result, and the distribution network is optimized according to the interaction result to generate the final optimization result of the distribution network, where the distribution network optimization operation model is constructed from the operation data of the distribution network.

[0117] Among them, the embodiments of the present application can input at least one of the voltage, current, and power status information of each node in the distribution network and the output information of distributed power sources into a pre-constructed distribution network optimization operation model, and interact the coupling information between the edge cluster and the cloud control platform based on the distribution network optimization operation model to generate an interaction result, and optimize the operation of the distribution network according to the interaction result to generate the final optimization result of the distribution network, where the distribution network optimization operation model is constructed from the operation data of the distribution network.

[0118] Optionally, in an embodiment of the present application, the distribution network is optimized according to the optimized operation model of the distribution network to generate the final optimization result of the distribution network, including: in the case of cloud-edge collaborative optimization scheduling, unifying the boundary coupling constraints into the objective function to construct the augmented Lagrangian function corresponding to the first cluster and the second cluster; determining the consistency global variables of the first cluster and the second cluster according to the fixed reference values iterated by the first cluster and the second cluster, and updating the consistency global variables and the augmented Lagrangian function to generate the updated consistency global variables and the updated Lagrangian multiplier vector; based on the updated consistency global variables and the updated Lagrangian multiplier vector, calculating the primal residual and the dual residual of the cluster optimization scheduling, and determining whether the primal residual and the dual residual meet the preset convergence condition; if the primal residual and the dual residual meet the preset convergence condition, stop the iteration of the solution of the distribution network optimization operation model and generate the final optimization result of the distribution network.

[0119] It can be understood that, as Figure 4 shown, the embodiments of the present application may include the following steps:

[0120] Step S401: Establish the sub-problem model of the edge cluster A.

[0121] Step S402: Solve the edge cluster A.

[0122] Step S403: Update the consistency variable Update the Lagrangian multiplier

[0123] Step S404: Establish the master problem model of the cloud control platform.

[0124] Step S405: Solve the master problem model.

[0125] Step S406: Update the consistency variable Update the Lagrangian multiplier

[0126] Step S407: Determine whether the convergence criterion is met. If so, execute Step S405; otherwise, execute Step S402.

[0127] Step S408: End the iteration and obtain the result.

[0128] Figure 4 The method for solving the distribution network optimization model based on cloud-edge collaboration specifically includes: original model conversion, updating consistency variables, and convergence discrimination.

[0129] (1) Among them, in an embodiment of the present application, for the original model conversion, during the cloud-edge collaborative optimization scheduling, each cluster performs independent optimization scheduling and then transmits the boundary coupling variables. Therefore, in the cloud-edge collaborative scheduling framework, the boundary coupling constraints are unified into the objective function, and the first cluster and the second cluster corresponding augmented Lagrangian functions and The calculation formulas are as follows:

[0130]

[0131] Among them, k is the number of iterations, and are the augmented Lagrangian multiplier vectors corresponding to clusters and respectively, each containing 12 elements, such as ρ is the penalty parameter.

[0132] (2) Update of consistency variables: and represent the fixed reference values of the (k + 1)-th iteration of clusters and respectively, which are consistency global variables and are obtained from the average value of the boundary coupling constraints obtained by the two clusters in the k-th iteration. The calculation formulas for the consistency global variables of the first cluster and the second cluster are:

[0133]

[0134] Among them, and are the boundary coupling variables during the internal solution of the two clusters at the k-th iteration, and are the boundary coupling variables interacted with the cloud control platform respectively.

[0135] (3) Convergence criterion. Determine whether the primal residual and the dual residual meet the convergence accuracy. The primal residual and the dual residual are expressed as follows:

[0136]

[0137] If both the primal residual and the dual residual are considered, the final iteration termination criterion is:

[0138]

[0139] Among them, ε is the set convergence accuracy.

[0140] Specifically, the overall solution steps are as follows:

[0141] 1) Initialization. Set the initial number of iterations to k = 0, and given the clusters and the clusters initial values of the Lagrange multipliers and given the initial values of the global consistency variables and ; Set the convergence accuracy ε for terminating the iteration.

[0142] 2) Independent cluster solution. In the (k + 1)-th iteration, solve the optimization problems of each cluster to obtain the local optimal scheduling solutions that minimize the augmented Lagrangian function and Meanwhile, obtain the latest boundary coupling variables and

[0143]

[0144] 3) Update the consistency constraints. Adjacent clusters exchange boundary coupling variable information and to update the consistency constraint variables, which are used as the fixed reference values for the next iteration.

[0145] 4) Update of the Lagrange multiplier vectors of the clusters and :

[0146] 5) Convergence judgment. Calculate the primal residual and the dual residual. If the convergence accuracy is met, stop the iteration; otherwise, jump to step 6). Among them, in this application, only the dual residual is used as the algorithm convergence criterion.

[0147] 6) Let k = k + 1 and return to step 2).

[0148] According to the method for optimizing the operation of a distribution network based on cloud-edge collaboration proposed in the embodiments of the present application, with cloud-edge collaboration as the core, a new operation control architecture for the distribution network is constructed. By deeply integrating "operation data + artificial intelligence" with the operation control technology of the distribution system, the limitations of existing methods are broken through, the operation control ability of the distribution network is effectively improved, and an optimal strategy is provided for the safe and stable operation of the distribution network under the condition of high-proportion distributed power access. Thus, the problems in the related technologies are solved, that is, the mode requires collecting and processing a large amount of measurement data, bringing high communication and computing costs; frequent communication is still required between regional controllers, and multiple iterations are needed to obtain the final optimization result; due to the inability to fully coordinate and utilize the controllable resources in the entire distribution system, its adjustment ability is relatively limited.

[0149] Next, describe the device for optimizing the operation of a distribution network based on cloud-edge collaboration proposed in the embodiments of the present application with reference to the accompanying drawings.

[0150] Figure 5 It is a schematic structural diagram of a distribution network optimal operation device based on cloud-edge collaboration according to an embodiment of the present application.

[0151] As Figure 5 shown, the distribution network optimal operation device 10 based on cloud-edge collaboration is applied to the model construction stage and includes: a construction module 100, an establishment module 200, and an optimization module 300.

[0152] The construction module 100 is used to construct a regulation and control architecture for cloud-edge collaboration of the distribution network with cloud-edge collaboration as the core.

[0153] The establishment module 200 is used to establish an optimal operation model for the cloud-edge collaborative distribution network based on the operation data of the distribution network according to the regulation and control architecture of the cloud-edge collaborative distribution network.

[0154] The optimization module 300 is used to construct the optimal operation model of the cloud-edge collaborative distribution network into a main problem model of the cloud control platform and a sub-problem model of the edge cluster, so as to optimize and output the final optimization result of the distribution network by using the main problem model of the cloud control platform and the sub-problem model of the edge cluster.

[0155] Optionally, in an embodiment of the present application, the distribution network optimal operation device 10 based on cloud-edge collaboration further includes: a first determination module and a second determination module.

[0156] Among them, the first determination module is used to, after constructing the optimal operation model of the cloud-edge collaborative distribution network into the main problem model of the cloud control platform and the sub-problem model of the edge cluster, determine the constraint conditions of the main problem model of the cloud control platform according to the first power flow constraint and the first safe operation constraint when the scheduling cost and system line loss of the main problem model of the cloud control platform meet the first preset minimization condition.

[0157] The second determination module is used to determine the constraint conditions of the sub-problem model of the edge cluster according to the second power flow constraint, the second safe operation constraint, and the boundary condition constraint when the internal network loss of the edge cluster in the sub-problem model of the edge cluster meets the second preset minimization condition.

[0158] Optionally, in an embodiment of the present application, the calculation formula of the first power flow constraint is:

[0159]

[0160] Among them, P ij , Q ij respectively represent the active and reactive power flows of branch ij, V i is the voltage amplitude of node i, G ij , B ij respectively represent the equivalent conductance and susceptance of branch ij, θ ijis the phase angle difference between the start and end of branch ij. The set π(i) represents the set of nodes directly connected to node i, and Q Gi is the reactive power injection of distributed photovoltaic and other generating units at node i, and Q Di is the reactive power load at node i;

[0161] The calculation formula for the first safe operation constraint is:

[0162]

[0163] Among them, P Gi,min , P Gi,max respectively represent the minimum and maximum active power outputs of distributed photovoltaic and other generating units at node i, and Q Gi,min , Q Gi,max respectively represent the minimum and maximum reactive power outputs of distributed photovoltaic and other generating units at node i, and V i,min , V i,max respectively represent the minimum and maximum voltage amplitudes of node i, and S ij,max respectively represent the transmission capacities of branch ij.

[0164] Optionally, in an embodiment of the present application, the calculation formula for the second power flow constraint is:

[0165]

[0166] Among them, I ij is the current amplitude of branch ij, and r ij , x ij respectively represent the resistance and reactance of branch ij. The set u(i) is the parent node of node i in the radial power grid, and the set v(i) is the child node of node i in the radial power grid;

[0167] The calculation formula for the second safety constraint is:

[0168]

[0169] Among them, I ij,max is the upper limit of the current amplitude of branch ij, and the subscript d represents the variables and parameters within the cluster;

[0170] The calculation formula for the boundary condition constraint is:

[0171]

[0172] Among them, respectively represent the equivalent active and reactive power loads of the kth cluster in the cloud control platform, respectively represent the active and reactive power injections from the cloud control platform to the root node of the kth cluster, respectively represent the voltage amplitude and phase angle of the node connecting the k-th cluster in the cloud control platform. respectively represent the voltage amplitude and phase angle of the k-th root node.

[0173] Such as Figure 6 As shown, the distribution network optimal operation device 20 based on cloud-edge collaboration is applied to the model application stage, including: an acquisition module 400 and a generation module 500.

[0174] Specifically, the acquisition module 400 is used to acquire at least one of the voltage, current, and power status information of each node in the distribution network and the output information of distributed power sources.

[0175] The generation module 500 is used to input at least one status information and output information into a pre-constructed distribution network optimal operation model, and based on the distribution network optimal operation model, interact the coupling information between the edge cluster and the cloud control platform to generate an interaction result, and optimize the operation of the distribution network according to the interaction result to generate the final optimization result of the distribution network, where the distribution network optimal operation model is constructed from the operation data of the distribution network.

[0176] Optionally, in an embodiment of the present application, the generation module 500 includes: a unification unit, an update unit, a judgment unit, and a generation unit.

[0177] Among them, the unification unit is used to unify the boundary coupling constraints into the objective function in the case of cloud-edge collaborative optimization scheduling to construct the augmented Lagrangian function corresponding to the first cluster and the second cluster.

[0178] The update unit is used to determine the consistency global variables of the first cluster and the second cluster according to the fixed reference values iterated by the first cluster and the second cluster, and update the consistency global variables and the augmented Lagrangian function to generate the updated consistency global variables and the updated Lagrange multiplier vector.

[0179] The judgment unit is used to calculate the primal residual and dual residual of the cluster optimization scheduling based on the updated consistency global variables and the updated Lagrange multiplier vector, and judge whether the primal residual and the dual residual meet the preset convergence conditions.

[0180] The generation unit is used to stop the iteration of the distribution network optimal operation model solution when the primal residual and the dual residual meet the preset convergence conditions, and generate the final optimization result of the distribution network.

[0181] Optionally, in an embodiment of the present application, the calculation formula of the augmented Lagrangian function corresponding to the first cluster and the second cluster is:

[0182]

[0183] where k is the number of iterations, and are the augmented Lagrange multiplier vectors corresponding to the clusters and respectively, and ρ is the penalty parameter;

[0184] The calculation formula for the consistency global variable of the first cluster and the second cluster is:

[0185]

[0186] where and are the boundary coupling variables during the internal solution of the two clusters at the k-th iteration respectively, and are the boundary coupling variables for interaction with the cloud control platform respectively.

[0187] It should be noted that the foregoing explanation of the embodiment of the method for optimizing the operation of a distribution network based on cloud-edge collaboration also applies to the device for optimizing the operation of a distribution network based on cloud-edge collaboration in this embodiment, and will not be elaborated here.

[0188] The device for optimizing the operation of a distribution network based on cloud-edge collaboration proposed in the embodiments of the present application takes cloud-edge collaboration as the core, constructs a new operation control architecture for the distribution network, and through the deep integration of "operation data + artificial intelligence" and distribution system operation control technology, breaks through the limitations of existing methods, effectively improves the operation control ability of the distribution network, and provides an optimal strategy for the safe and stable operation of the distribution network under the condition of high proportion of distributed power source access. Thus, it solves the problems in the related technologies that the mode requires collecting and processing a large amount of measurement data, bringing high communication and computing costs; frequent communication is still required between regional controllers, and multiple iterations are needed to obtain the final optimization result; due to the inability to fully coordinate and utilize the controllable resources in the entire distribution system, its adjustment ability is relatively limited.

[0189] Figure 7 The structure diagram of the electronic device provided in the embodiments of the present application. The electronic device may include:

[0190] A memory 701, a processor 702, and a computer program stored on the memory 701 and executable on the processor 702.

[0191] When the processor 702 executes the program, it implements the method for optimizing the operation of a distribution network based on cloud-edge collaboration provided in the above embodiments.

[0192] Further, the electronic device further includes:

[0193] A communication interface 703 for communication between the memory 701 and the processor 702.

[0194] A memory 701 for storing a computer program that can run on a processor 702.

[0195] The memory 701 may include a high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0196] If the memory 701, the processor 702, and the communication interface 703 are implemented independently, the communication interface 703, the memory 701, and the processor 702 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0197] Optionally, in a specific implementation, if the memory 701, the processor 702, and the communication interface 703 are integrated on a single chip, the memory 701, the processor 702, and the communication interface 703 can communicate with each other through an internal interface.

[0198] The processor 702 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0199] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0200] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0201] Any process or method description represented in a flowchart or otherwise described herein may be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of this application belong.

[0202] The logic and / or steps represented in a flowchart or otherwise described herein, for example, may be considered as a sequenced list of executable instructions for implementing a logical function, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or N wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium may even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0203] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0204] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program. The said program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0205] In addition, in each embodiment of the present application, the functional units can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into a module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0206] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A distribution network optimization operation method based on cloud-edge collaboration, characterized in that: Applied to the model building phase, it includes the following steps: With cloud-edge collaboration as the core, build a cloud-edge collaborative control architecture for distribution networks; Based on the distribution network cloud-edge collaborative control architecture, a cloud-edge collaborative distribution network optimization operation model is established according to the operation data of the distribution network; The cloud-edge collaborative distribution network optimization operation model is constructed as a cloud-side management and control platform main problem model and an edge-side cluster sub-problem model, so as to utilize the cloud-side management and control platform main problem model and the edge-side cluster sub-problem model to optimize and output the final optimization result of the distribution network.

2. The method according to claim 1, characterized in that After constructing the cloud-edge collaborative distribution network optimization operation model into a cloud-side management and control platform main problem model and an edge-end cluster sub-problem model, it also includes: When the scheduling cost and system network loss of the main problem model of the cloud control platform meet the first preset minimization condition, determining the constraint condition of the main problem model of the cloud control platform according to the first power flow constraint and the first safe operation constraint; When the internal network loss of the cluster of the edge cluster sub-problem model satisfies the second preset minimization condition, the constraint conditions of the edge cluster sub-problem model are determined according to the second power flow constraint, the second safe operation constraint and the boundary condition constraint.

3. The method according to claim 2, characterized in that The calculation formula of the first power flow constraint is: Among them, P ij ,Q ij Respectively represent the active and reactive power flows of branch ij, V i is the voltage amplitude of node i, G ij ,B ij denote the equivalent conductance and susceptance of the branch ij, θ ij is the phase angle difference between the first and the last branch ij, the set π(i) represents the set of nodes directly connected to the node i, Q Gi is the reactive power injection of distributed photovoltaic and other generators at the node i, Q Di is the reactive load at the node i; The calculation formula of the first safe operation constraint is: Among them, P Gi,min ,P Gi,max They represent the minimum and maximum active output of the distributed photovoltaic generator set at the node i, Q Gi,min ,Q Gi,max Respectively represent the minimum and maximum reactive power output of the distributed photovoltaic generator set at the node i, V i,min ,V i,max Respectively represent the minimum and maximum voltage amplitudes of the node i, S ij,max Respectively represent the transmission capacity of the branch ij.

4. The method according to claim 3, characterized in that The calculation formula of the second power flow constraint is: Among them, I ij is the current amplitude of the branch ij, r ij ,x ij Respectively represent the resistance and reactance of the branch ij, set u(i) is the parent node of the node i in the radial power grid, and set v(i) is the child node of the node i in the radial power grid; The calculation formula of the second safety constraint is: Among them, I ij,max is the upper limit of the current amplitude of the branch ij, and the subscript d represents the variables and parameters within the cluster; The calculation formula of the boundary condition constraint is: in, They represent the equivalent active and reactive loads of the kth cluster in the cloud management and control platform, They represent the active and reactive power injection from the cloud control platform to the k-th cluster root node, respectively. represent the voltage amplitude and phase angle of the node connected to the kth cluster in the cloud control platform, respectively, represent the voltage amplitude and phase angle of the kth root node respectively.

5. A distribution network optimization operation method based on cloud-edge collaboration, characterized in that: Applied to the model application phase, it includes the following steps: Obtaining at least one status information of voltage, current and power of each node in the distribution network and output information of the distributed power source; The at least one status information and the output information are input into a pre-built distribution network optimization operation model, and based on the distribution network optimization operation model, the coupling information of the edge cluster and the cloud management and control platform is interacted to generate an interaction result, and the distribution network is optimized and operated according to the interaction result to generate a final optimization result of the distribution network, wherein the distribution network optimization operation model is constructed by the operation data of the distribution network.

6. The method according to claim 5, characterized in that Optimizing the distribution network according to the distribution network optimization operation model to generate a final optimization result of the distribution network includes: In the case of cloud-edge collaborative optimization scheduling, the boundary coupling constraints are unified into the objective function to construct the augmented Lagrangian functions corresponding to the first cluster and the second cluster; Determine consistent global variables of the first cluster and the second cluster according to iterative fixed reference values ​​of the first cluster and the second cluster, and update the consistent global variables and the augmented Lagrangian function to generate updated consistent global variables and an updated Lagrangian multiplier vector; Based on the updated consistency global variable and the updated Lagrange multiplier vector, the original residual and the dual residual of the cluster optimization scheduling are calculated, and it is determined whether the original residual and the dual residual meet the preset convergence condition; If the original residual and the dual residual meet the preset convergence condition, the iteration of solving the distribution network optimization operation model is stopped to generate the final optimization result of the distribution network.

7. The method according to claim 6, characterized in that The calculation formula of the augmented Lagrangian function corresponding to the first cluster and the second cluster is: Where k is the number of iterations, and Cluster and The corresponding augmented Lagrange multiplier vector, ρ is the penalty parameter; The calculation formula of the consistency global variable of the first cluster and the second cluster is: in, and are the boundary coupling variables when solving the internal problems of the two clusters at the kth iteration, and They are the boundary coupling variables of the interaction between cloud management and control platforms.

8. A distribution network optimization operation device based on cloud-edge collaboration, characterized in that: Applied in the model building phase, including: A construction module is used to build a cloud-edge collaborative control architecture for distribution networks with cloud-edge collaboration as the core; Establish a module for establishing a cloud-edge collaborative distribution network optimization operation model based on the distribution network cloud-edge collaborative control architecture and according to the distribution network operation data; The optimization module is used to construct the cloud-edge collaborative distribution network optimization operation model into a cloud-side management and control platform main problem model and an edge-end cluster sub-problem model, so as to utilize the cloud-side management and control platform main problem model and the edge-end cluster sub-problem model to optimize and output the final optimization result of the distribution network.

9. A distribution network optimization operation device based on cloud-edge collaboration, characterized in that: Applied in the model application phase, including: An acquisition module, used to acquire at least one status information of voltage, current and power of each node in the distribution network and output information of the distributed power source; A generation module is used to input the at least one status information and the output information into a pre-built distribution network optimization operation model, and based on the coupling information of the distribution network optimization operation model, interact with the edge cluster and the cloud management and control platform to generate an interaction result, and optimize the operation of the distribution network according to the interaction result to generate a final optimization result of the distribution network, wherein the distribution network optimization operation model is constructed by the operation data of the distribution network.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distribution network optimization operation method based on cloud-edge collaboration as described in any one of claims 1 to 4 or the distribution network optimization operation method based on cloud-edge collaboration as described in any one of claims 5 to 7.

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