Multi-microgrid group optimization scheduling method, device and system based on cloud edge collaboration

Abstract

The invention provides a multi-microgrid group optimization scheduling method, device and system based on cloud edge cooperation. A cloud server receives local scheduling parameters obtained and sent by each edge server through a microgrid scheduling model; the cloud server obtains an initial global scheduling parameter based on each local scheduling parameter and the power distribution network scheduling model; and for each edge server, executing an interactive iteration process based on the initial global scheduling parameter and a distributed optimization algorithm until an optimal global scheduling parameter is obtained. In the scheme, the data transmission quantity is effectively reduced and the overall scheduling efficiency of the system is improved through a cloud-edge collaborative hierarchical structure, and only the scheduling parameters are transmitted between the cloud layer and the edge layer, and sensitive information of the micro-grid is not acquired by the cloud layer, so that data isolation and privacy security among subjects are ensured, and the security of the micro-grid is improved. And in addition, a global optimal scheduling result is obtained by performing multiple interactive iterations by using a distributed optimization algorithm, so that the overall economy and the operation efficiency of the system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-microgrid cluster optimization scheduling in power systems, and specifically to a multi-microgrid cluster optimization scheduling method, device and system based on cloud-edge collaboration. Background Art

[0002] With the transformation of the global energy structure and the continuous development of distributed energy technology, microgrid technology has become an important part of the future power system. Microgrids can flexibly integrate various renewable energy sources, such as solar photovoltaic power generation, wind power generation, etc., and can also include energy storage systems, fuel power generation equipment, etc., to provide power users with efficient and low-carbon power supply. This distributed energy system not only improves the stability and reliability of local power supply, but also reduces power costs by participating in power market transactions, bringing economic benefits to users.

[0003] However, with the expansion of microgrid scale and widespread application, the concept of multi-microgrid cluster system has gradually emerged. Multi-microgrid cluster refers to multiple independently operated microgrids interconnected through a common active distribution network to form an energy system with stronger coupling capabilities. In this system, each microgrid can not only operate independently to meet its local load demand, but also participate in the dispatch optimization of the distribution network to jointly improve the economy and reliability of the overall system.

[0004] Although the multi-microgrid system has broad application prospects, its economic dispatch optimization faces many challenges. First, the traditional centralized dispatch method needs to collect detailed operating data of each microgrid (such as power generation, load conditions, etc.) and optimize them centrally through a unified dispatch center. However, this method has the risk of data privacy leakage. As an independent interest subject, each microgrid is unwilling to share its detailed operating data. In addition, as the scale of the multi-microgrid system expands, centralized dispatching will also bring huge communication burden, resulting in reduced system operation efficiency. Summary of the invention

[0005] In view of this, the embodiments of the present invention provide a multi-microgrid group optimization scheduling method, device and system based on cloud-edge collaboration, so as to achieve the purpose of ensuring data isolation and privacy security between various entities, and ensuring the overall economy and operation efficiency of the system.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] The first aspect of the embodiment of the present invention discloses a multi-microgrid cluster optimization scheduling method based on cloud-edge collaboration, which is applied to a cloud server in a multi-microgrid cluster optimization scheduling system based on cloud-edge collaboration, wherein the cloud server is communicatively connected with a plurality of edge servers in the multi-microgrid cluster optimization scheduling system, the cloud server is deployed in a distribution network, and each edge server is deployed in a corresponding microgrid, and the method comprises:

[0008] Receiving local scheduling parameters sent by each edge server; the local scheduling parameters are obtained by the edge server based on a pre-configured microgrid scheduling model, and the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid;

[0009] Based on each of the local dispatching parameters and a pre-configured distribution network dispatching model, an initial global dispatching parameter is obtained; the distribution network dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network;

[0010] For each edge server, an interactive iteration process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0011] Preferably, for each edge server, performing an interactive iteration process based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained includes:

[0012] Sending initial global scheduling parameters to each of the edge servers;

[0013] Receiving local optimization results obtained by each edge server performing a local optimization task based on the initial global scheduling parameters;

[0014] Performing a global optimization task based on each of the local optimization results to obtain a new global scheduling parameter;

[0015] If the new global scheduling parameters do not meet the convergence conditions, the new global scheduling parameters are used as the initial global scheduling parameters, and the step of sending the initial global scheduling parameters to each edge server is returned to execute until the new global scheduling parameters meet the convergence conditions to obtain the optimal global scheduling parameters.

[0016] Preferably, the receiving of local optimization results obtained by each edge server performing a local optimization task based on the initial global scheduling parameters includes:

[0017] For each edge server whose remaining computing resources support executing the local optimization task based on the initial global scheduling parameters, receiving a local optimization result obtained by executing the local optimization task from the edge server;

[0018] For a target edge server whose remaining computing resources do not support executing a local optimization task based on the initial global scheduling parameters, a local optimization result sent by an outsourced computing object specified by the target edge server is received; the outsourced computing object obtains the local optimization result by receiving an outsourced task sent by the target edge server and executing the outsourced task; the outsourced task is obtained by the target edge server by encrypting and packaging the local optimization task using outsourcing encryption technology; the outsourced object includes: the cloud server and the edge server whose remaining computing resources support executing the local optimization task.

[0019] Preferably, before executing the interactive iteration process based on the initial global scheduling parameters and the distributed optimization algorithm for each edge server, the method further comprises:

[0020] Get initial condition parameters;

[0021] The initial condition parameters are input into the distributed optimization algorithm for initialization setting.

[0022] The second aspect of the embodiment of the present invention discloses a multi-microgrid group optimization scheduling method based on cloud-edge collaboration, which is applied to any edge server in a multi-microgrid group optimization scheduling system based on cloud-edge collaboration, each of the edge servers is communicatively connected with a cloud server in the multi-microgrid group optimization scheduling system, each of the edge servers is deployed in a corresponding microgrid, and the cloud server is deployed in a distribution network, and the method includes:

[0023] Based on a pre-configured microgrid dispatching model, local dispatching parameters are obtained, and the local dispatching parameters are sent to the cloud server, so that the cloud server obtains initial global dispatching parameters based on the local dispatching parameters sent by each edge server and the pre-configured distribution network dispatching model; the microgrid dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; the distribution network dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network;

[0024] An interactive iterative process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0025] Preferably, the interactive iteration process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained, including:

[0026] Receiving initial global scheduling parameters sent by the cloud server;

[0027] Execute local optimization tasks based on the initial global scheduling parameters to obtain local optimization results;

[0028] Sending the local optimization results to the cloud server, so that the cloud server performs the global optimization task based on the local optimization results sent by each edge server to obtain a new global scheduling parameter. If the new global scheduling parameter does not meet the convergence condition, the new global scheduling parameter is used as the initial global scheduling parameter;

[0029] Return to the step of receiving the initial global scheduling parameters sent by the cloud server until the cloud server determines that the new global scheduling parameters meet the convergence condition and obtains the optimal global scheduling parameters.

[0030] Preferably, the performing of the local optimization task based on the initial global scheduling parameters to obtain the local optimization result includes:

[0031] If the remaining computing resources support executing the local optimization task based on the initial global scheduling parameters, then executing the local optimization task to obtain a local optimization result;

[0032] If the remaining computing resources do not support the execution of the local optimization task based on the initial global scheduling parameters, the local optimization task is encrypted and packaged using the outsourcing encryption technology to obtain an outsourced task;

[0033] The outsourced task is sent to any outsourced computing object, so that the outsourced computing object executes the outsourced task to obtain the local optimization result, and sends the local optimization result to the cloud server; the outsourced object includes: the cloud server and the edge server whose remaining computing resources support the execution of the local optimization task.

[0034] The third aspect of the embodiment of the present invention discloses a multi-microgrid group optimization scheduling device based on cloud-edge collaboration, which is applied to a cloud server in a multi-microgrid group optimization scheduling system based on cloud-edge collaboration, wherein the cloud server is communicatively connected with a plurality of edge servers in the multi-microgrid group optimization scheduling system, the cloud server is deployed in a distribution network, and each edge server is deployed in a corresponding microgrid, and the device comprises:

[0035] A receiving unit, configured to receive local scheduling parameters sent by each edge server; the local scheduling parameters are obtained by the edge server based on a pre-configured microgrid scheduling model, and the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid;

[0036] A first initial dispatching unit, configured to obtain initial global dispatching parameters based on each of the local dispatching parameters and a pre-configured distribution network dispatching model; the distribution network dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network;

[0037] The first iteration unit is used to perform an interactive iteration process for each edge server based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0038] The fourth aspect of the embodiment of the present invention discloses a multi-microgrid group optimization scheduling device based on cloud-edge collaboration, which is applied to any edge server in a multi-microgrid group optimization scheduling system based on cloud-edge collaboration, each of the edge servers is communicatively connected with a cloud server in the multi-microgrid group optimization scheduling system, each of the edge servers is deployed in a corresponding microgrid, and the cloud server is deployed in a distribution network, and the device includes:

[0039] A second initial scheduling unit is used to obtain local scheduling parameters based on a pre-configured microgrid scheduling model, and send the local scheduling parameters to the cloud server, so that the cloud server obtains initial global scheduling parameters based on the local scheduling parameters sent by each edge server and the pre-configured distribution network scheduling model; the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; the distribution network scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network;

[0040] The second iteration unit is used to perform an interactive iteration process based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0041] A fifth aspect of an embodiment of the present invention discloses a multi-microgrid group optimization and scheduling system based on cloud-edge collaboration, the system comprising: a cloud server and multiple edge servers, the cloud server is communicatively connected with multiple edge servers in the multi-microgrid group optimization and scheduling system, the cloud server is deployed in a distribution network, and each of the edge servers is deployed in a corresponding microgrid;

[0042] The cloud server is used to receive the local dispatch parameters sent by each edge server; based on each local dispatch parameter and a pre-configured distribution network dispatch model, an initial global dispatch parameter is obtained; the distribution network dispatch model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network; for each edge server, an interactive iterative process is performed based on the initial global dispatch parameters and a distributed optimization algorithm until the optimal global dispatch parameter is obtained;

[0043] The edge server is used to obtain local scheduling parameters based on a pre-configured microgrid scheduling model and send the local scheduling parameters to the cloud server; the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; based on the initial global scheduling parameters and the distributed optimization algorithm, an interactive iterative process is performed until the optimal global scheduling parameters are obtained.

[0044] A multi-microgrid cluster optimization scheduling method, device and system based on cloud-edge collaboration provided based on the above-mentioned embodiments of the present invention are applied to a cloud server in a multi-microgrid cluster optimization scheduling system based on cloud-edge collaboration, the cloud server is communicatively connected to multiple edge servers in the multi-microgrid cluster optimization scheduling system, the cloud server is deployed in a distribution network, and each of the edge servers is deployed in a corresponding microgrid. The method includes: receiving local scheduling parameters sent by each of the edge servers; the local scheduling parameters are obtained by the edge server based on a pre-configured microgrid scheduling model, and the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; based on each of the local scheduling parameters and the pre-configured distribution network scheduling model, an initial global scheduling parameter is obtained; the distribution network scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network; for each of the edge servers, an interactive iterative process is performed based on the initial global scheduling parameters and a distributed optimization algorithm until the optimal global scheduling parameters are obtained. In this scheme, the layered structure of cloud-edge collaboration effectively reduces the amount of data transmission and improves the overall scheduling efficiency of the system. Only scheduling parameters are transmitted between the cloud layer and the edge layer, and sensitive information of the microgrid will not be obtained by the cloud layer, thereby ensuring data isolation and privacy security between various entities. In addition, the distributed optimization algorithm is used for multiple interactive iterations to obtain the global optimal scheduling result, ensuring the overall economy and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0046] Figure 1 An architecture diagram of a multi-microgrid cluster optimization scheduling system based on cloud-edge collaboration disclosed in an embodiment of the present invention;

[0047] Figure 2 A technical schematic diagram of a multi-microgrid cluster optimization scheduling system based on cloud-edge collaboration disclosed in an embodiment of the present invention;

[0048] Figure 3 A flowchart of a multi-microgrid group optimization scheduling method based on cloud-edge collaboration disclosed in an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of an encryption outsourcing computing strategy disclosed in an embodiment of the present invention;

[0050] Figure 5A flowchart of another multi-microgrid group optimization scheduling method based on cloud-edge collaboration disclosed in an embodiment of the present invention;

[0051] Figure 6 A structural diagram of a multi-microgrid group optimization scheduling device based on cloud-edge collaboration disclosed in an embodiment of the present invention;

[0052] Figure 7 This is a structural diagram of another multi-microgrid cluster optimization scheduling device based on cloud-edge collaboration disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0055] As can be seen from the background technology, the traditional centralized dispatching method needs to collect detailed operating data of each microgrid (such as power generation, load conditions, etc.) and optimize it centrally through a unified dispatching center. However, this method has the risk of data privacy leakage. As an independent interest subject, each microgrid is unwilling to share its detailed operating data. In addition, as the scale of multi-microgrid group systems expands, centralized dispatching will also bring huge communication burdens, resulting in reduced system operation efficiency.

[0056] Therefore, the embodiment of the present invention discloses a multi-microgrid group optimization scheduling method, device and system based on cloud-edge collaboration. In this solution, the layered structure of cloud-edge collaboration effectively reduces the amount of data transmission, improves the overall scheduling efficiency of the system, and only transmits scheduling parameters between the cloud layer and the edge layer, and the sensitive information of the microgrid will not be obtained by the cloud layer, thereby ensuring data isolation and privacy security between the subjects. In addition, the distributed optimization algorithm is used to perform multiple interactive iterations to obtain the global optimal scheduling result, which ensures the overall economy and operation efficiency of the system.

[0057] like Figure 1As shown, it is an architecture diagram of a multi-microgrid cluster optimization scheduling system based on cloud-edge collaboration disclosed in an embodiment of the present invention. The system includes: a computing layer and a physical layer.

[0058] Among them, the computing layer includes: cloud servers and multiple edge servers, and the physical layer includes: distribution network and multiple microgrids.

[0059] The cloud server is connected to each edge server in communication. The cloud server is deployed in the distribution network, and each edge server is deployed in the corresponding microgrid.

[0060] The distribution network and multiple microgrids operate independently, and the distribution network and multiple microgrids are physically connected to exchange power.

[0061] The method for constructing a multi-microgrid cluster optimization scheduling system with a two-layer architecture of physical layer and computing layer is as follows:

[0062] In order to meet the needs of joint operation and real-time regulation between the distribution network and the microgrid, a microgrid is used as an edge node and an edge server is deployed to provide computing, local optimization scheduling and data storage capabilities. The cloud server is deployed in the distribution network and has abundant computing resources. It is mainly responsible for global optimization scheduling and handling complex scheduling problems.

[0063] like Figure 2 As shown, it is a technical principle diagram of a multi-microgrid group optimization scheduling system based on cloud-edge collaboration disclosed in an embodiment of the present invention.

[0064] Each microgrid is a distributed architecture, which includes power generation equipment (such as wind power generation, photovoltaic power generation, energy storage system, etc.) and load equipment. The distribution network includes generator sets and load equipment.

[0065] Specifically, the cloud server is used to receive the local scheduling parameters sent by each edge server; based on each local scheduling parameter and a pre-configured distribution network scheduling model, the initial global scheduling parameters are obtained; for each edge server, an interactive iterative process is performed based on the initial global scheduling parameters and a distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0066] Among them, the distribution network dispatching model is established based on the objective function with the goal of minimizing the total operating cost of the distribution network.

[0067] The edge server is used to obtain local scheduling parameters based on a pre-configured microgrid scheduling model and send the local scheduling parameters to the cloud server; an interactive iterative process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0068] Among them, the microgrid dispatching model is established based on the objective function with the goal of minimizing the total operating cost of the microgrid.

[0069] It should be noted that the local scheduling parameters refer to the transaction power given within the initial microgrid with the goal of minimizing the total operating cost of the microgrid, and the global scheduling parameters are the set of transaction powers transmitted between the cloud server and each edge server.

[0070] Based on the above-mentioned embodiment of the present invention, a multi-microgrid group optimization and scheduling system based on cloud-edge collaboration is disclosed. Each microgrid can independently generate electricity, store energy and meet local load needs. At the same time, through collaborative cooperation with other microgrids and distribution networks, higher economy, reliability and flexibility can be achieved. The multi-microgrid group system can not only optimize the utilization of energy resources in the region, but also improve the operating efficiency of the overall system through distributed scheduling, and adapt to the needs of modern power systems for clean energy consumption and multi-subject optimized operation. In addition, by distributing computing tasks to cloud servers and edge servers, distributed economic scheduling optimization can be achieved without sharing sensitive data. This layered architecture can not only reduce the communication burden, but also ensure the data privacy security of each microgrid.

[0071] like Figure 3 As shown, it is a flowchart of a multi-microgrid group optimization scheduling method based on cloud-edge collaboration disclosed in an embodiment of the present invention, which is applied to a cloud server in a multi-microgrid group optimization scheduling system based on cloud-edge collaboration disclosed in the above embodiment of the present invention, and includes the following steps:

[0072] Step S301: receiving local scheduling parameters sent by each edge server.

[0073] In step S301, local dispatch parameters are obtained by the edge server based on a preconfigured microgrid dispatch model, and the microgrid dispatch model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid.

[0074] Specifically, the local dispatch parameters are calculated by the edge server based on the pre-configured microgrid dispatch model and state parameters of the microgrid, such as local load, power generation capacity, and energy storage status.

[0075] It should be noted that the microgrid dispatch model obtains the internal dispatch parameters only in the edge server, such as the output of the controllable units in the microgrid, while obtaining the local dispatch parameters. The internal dispatch parameters retained in the edge server will also be optimized and updated in the subsequent interactive iteration process until the optimal global dispatch parameters are obtained. The edge server performs power dispatch externally according to the global dispatch parameters, and implements dispatch within the microgrid according to the current internal dispatch parameters.

[0076] Step S302: Based on various local dispatch parameters and a pre-configured distribution network dispatch model, initial global dispatch parameters are obtained.

[0077] In step S302, a distribution network dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network.

[0078] It should be noted that the distribution network dispatch model obtains the global dispatch parameters as well as the internal dispatch parameters that are only retained in the cloud server, such as the output of the controllable units within the distribution network. The internal dispatch parameters retained in the cloud server will also be optimized and updated in the subsequent interactive iteration process until the optimal global dispatch parameters are obtained. The cloud server performs power dispatch externally according to the global dispatch parameters, and implements dispatch within the distribution network according to the current internal dispatch parameters.

[0079] In the specific implementation process of step S302, the global dispatching parameters and the internal dispatching parameters of the distribution network are calculated based on the local dispatching parameters, the pre-configured distribution network dispatching model and the state parameters of the distribution network.

[0080] The status parameters of the distribution network include: local load, power generation capacity, energy storage status, etc.

[0081] Among them, the objective function aimed at minimizing the total operating cost of the distribution network is the objective function aimed at minimizing the sum of the total power generation cost of the distribution network units and the interaction cost with multiple microgrids:

[0082] minC=min{C DG +C MG}

[0083] Among them, C is the total operating cost of the distribution network, C DG is the total power generation cost of the distribution network units, C MG is the interaction cost between the distribution network and each microgrid.

[0084]

[0085] Where N is the total number of generator sets in the distribution network, P DG,i is the power generation of generator set i, a i , b i and c i is the cost coefficient of generator set i.

[0086]

[0087] Where M is the total number of microgrids, P buy,j and P sell,j are the power purchase and sales from the distribution network to microgrid j, c buy,j and c sell,j are the electricity purchase price and electricity sales price of microgrid j respectively.

[0088] The constraints included in the distribution network dispatching model are as follows:

[0089] Active power balance constraints, system reserve constraints, upper and lower limits of active power of controllable fuel units, climbing speed constraints of controllable fuel units, minimum start and stop time constraints, large power grid power purchase constraints, removable load constraints and PCC (Point of Common Coupling) transaction power constraints.

[0090] Step S303: for each edge server, an interactive iteration process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0091] In step S303, the distributed optimization algorithm can handle multi-objective and multi-dimensional scheduling optimization problems, while reducing system costs and ensuring the overall economy and operating efficiency of the system.

[0092] In one embodiment, after the optimal global scheduling parameters are obtained, the optimal global scheduling parameters are sent to each edge server.

[0093] It should be noted that in the interactive iteration process, not only the global scheduling parameters are iteratively updated, but also the internal scheduling parameters that do not need to be transmitted within the cloud server are iteratively updated. While obtaining the optimal global scheduling parameters, the optimal internal scheduling parameters for the distribution network are obtained. In addition, the internal scheduling parameters that do not need to be transmitted within the edge server are iteratively updated to obtain the optimal internal scheduling parameters for the microgrid.

[0094] The optimal internal dispatch parameters and the optimal global dispatch parameters corresponding to the distribution network and the microgrid constitute the day-ahead dispatch plan. Since the microgrid and the distribution network directly know the parts of the day-ahead dispatch plan that are relevant to each other, the microgrid and the distribution network can achieve global dispatch by directly regulating the output of the internal units according to these parts.

[0095] Among them, Day-Ahead Scheduling is a planned scheduling plan formulated in the power market or power grid scheduling to ensure the balance of power supply and demand the next day. Its essence is to make optimal arrangements for power generation, transmission and distribution one day in advance based on the demand forecast of the power market, the availability of power generation units and other system constraints.

[0096] In one embodiment, before step S303, the method further includes:

[0097] Obtain initial condition parameters; input the initial condition parameters into the distributed optimization algorithm for initialization settings.

[0098] The initial condition parameters include: load demand, environmental information, predicted power generation of new energy units and other parameters.

[0099] It should be noted that the initial condition parameters are obtained from historical data and prediction models. In addition to the decision variables in the distributed optimization algorithm, other variables need to be given in advance to solve the decision variables. The decision variables are the internal dispatching parameters of the microgrid and the distribution network and the dispatching parameters between the distribution network and the microgrid.

[0100] The specific implementation process of step S303 includes:

[0101] Step S3031: Send the initial global scheduling parameters to each edge server.

[0102] Step S3032: receiving local optimization results obtained by each edge server performing a local optimization task based on the initial global scheduling parameters.

[0103] In step S3032, when the local optimization task is executed, the edge server locally optimizes the internal scheduling parameters and the transaction power transmitted to the cloud server according to the local load, power generation capacity, energy storage status and other state parameters. The local optimization result refers to the transaction power transmitted to the cloud server.

[0104] For detailed description of the specific implementation process of step S3032, see Figure 4 , is a schematic diagram of an encryption outsourcing computing strategy disclosed in an embodiment of the present invention, and the specific implementation process is as follows:

[0105] For each edge server whose remaining computing resources support executing the local optimization task based on the initial global scheduling parameters, a local optimization result obtained by executing the local optimization task is received from the edge server.

[0106] For a target edge server whose remaining computing resources do not support executing a local optimization task based on the initial global scheduling parameters, a local optimization result sent by an outsourced computing object specified by the target edge server is received.

[0107] Among them, the outsourced computing object receives the outsourced tasks sent by the target edge server and executes the outsourced tasks to obtain the local optimization results; the outsourced tasks are obtained by the target edge server by encrypting and packaging the local optimization tasks using outsourcing encryption technology; the outsourced objects include: cloud servers and edge servers whose remaining computing resources support the execution of local optimization tasks.

[0108] In an embodiment of the present invention, when the computing resources of each edge server can support the completion of their respective local optimization tasks within the allowed time, each edge server solves in parallel, and the distribution network and the microgrid cloud-edge collaboratively exchange non-sensitive information to achieve coordinated solution of the distribution network and the microgrid. However, when the computing resources of the edge node are insufficient to support the execution of local optimization tasks, the overall solution time of the cloud-edge collaboration will be greatly reduced due to the cumulative lag effect of a certain edge server. At this time, the edge server with scarce computing resources can outsource the computing tasks to the cloud server or other edge servers to effectively improve the computing efficiency.

[0109] It should be noted that when the outsourced task is executed by the outsourced computing object, it is equivalent to executing the local optimization task based on the initial global scheduling parameters. Since the local optimization task contains the state parameters of the edge server such as local load, power generation capacity, energy storage status, etc., which are sensitive power data, the encrypted outsourced computing technology is adopted. Whether the outsourced computing object is a cloud server or other edge server, the real privacy information cannot be obtained, and the risk of information leakage caused by potential hacker attacks is also avoided.

[0110] Preferably, if the outsourced computing object is a cloud server, the cloud server receives the outsourced task sent by the edge server, and executes the outsourced task to obtain a local optimization result.

[0111] Step S3033: Execute the global optimization task based on each local optimization result to obtain a new global scheduling parameter.

[0112] In step S3033, the cloud server performs the global optimization task, calculates the global power balance and economic benefits, and gives new global scheduling parameters.

[0113] It should be noted that the global optimization task can also update the internal scheduling parameters of the cloud server.

[0114] Step S3034: If the new global scheduling parameters do not meet the convergence conditions, the new global scheduling parameters are used as the initial global scheduling parameters, and the process returns to step S3031 until the new global scheduling parameters meet the convergence conditions to obtain the optimal global scheduling parameters.

[0115] Among them, the new global scheduling parameters meet the convergence conditions, that is, the global economic benefits and system power balance reach the optimal state.

[0116] Based on the above-mentioned multi-microgrid group optimization scheduling method based on cloud-edge collaboration disclosed by the present invention, in this scheme, the entire system is divided into two parts, cloud server and edge server, through a hierarchical structure, so as to realize the organic combination of global and local scheduling. As the core of global scheduling, the cloud server is responsible for collecting and processing power data and economic scheduling requirements at the distribution network level, comprehensively considering factors such as system operating costs, power generation capacity and load demand, and formulating a global optimization scheduling strategy. At the same time, the cloud server and the edge server exchange data by transmitting transaction power without transmitting the specific operation data of each microgrid, ensuring the privacy of the data. The edge servers of each microgrid focus on processing local optimization tasks within the microgrid. The edge server uses local load, power generation and energy storage data to perform independent optimization calculations to meet local power requirements, and transmits the optimized transaction power to the cloud server to realize cloud-edge collaborative scheduling. Through this distributed computing mode, not only the communication burden of the system is greatly reduced, the demand for large-scale data transmission is reduced, but also the operating efficiency and response speed of the entire system are ensured. At the same time, the present invention uses encrypted outsourced computing technology to outsource local optimization tasks to cloud servers or other edge servers with sufficient computing resources when the computing resources of the edge server are insufficient, thereby further improving the flexibility and computing efficiency of the system.

[0117] like Figure 5 As shown, it is a flowchart of another multi-micronetwork group optimization scheduling method based on cloud-edge collaboration disclosed in an embodiment of the present invention, which is applied to an edge server in a multi-micronetwork group optimization scheduling system based on cloud-edge collaboration disclosed in the above embodiment of the present invention, including the following steps:

[0118] Step S501: local dispatching parameters are obtained based on a pre-configured microgrid dispatching model, and the local dispatching parameters are sent to a cloud server, so that the cloud server obtains initial global dispatching parameters based on the local dispatching parameters sent by each edge server and the pre-configured distribution network dispatching model.

[0119] Among them, the microgrid dispatching model is established based on the objective function with the goal of minimizing the total operating cost of the microgrid; the distribution network dispatching model is established based on the objective function with the goal of minimizing the total operating cost of the distribution network.

[0120] Among them, the objective function to minimize the total operating cost of the microgrid is the objective function to minimize the sum of the total power generation cost of the microgrid units and the interaction cost with the distribution network:

[0121] minf=f DG +f oper +f PD

[0122] Where f is the sum of the operating costs of a single microgrid, f DG is the power generation cost of the unit, considering only traditional generator sets such as diesel engines and steam turbines, f oper is the operation and maintenance cost of the new energy unit, f PD It is the transaction cost between microgrid and distribution network.

[0123] The constraints of the microgrid dispatch model include: power balance constraint, distributed generation power constraint and PCC transaction power constraint.

[0124] Step S502: performing an interactive iteration process based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0125] The specific implementation process of step S502 includes the following steps:

[0126] Step S5021: Receive the initial global scheduling parameters sent by the cloud server.

[0127] Step S5022: Execute the local optimization task based on the initial global scheduling parameters to obtain the local optimization result.

[0128] The specific implementation process of step S5022 is as follows:

[0129] If the remaining computing resources support executing the local optimization task based on the initial global scheduling parameters, the local optimization task is executed to obtain the local optimization result.

[0130] If the remaining computing resources do not support the execution of local optimization tasks based on the initial global scheduling parameters, the outsourcing encryption technology is used to encrypt and package the local optimization tasks to obtain outsourced tasks.

[0131] The outsourced task is sent to any outsourced computing object, so that the outsourced computing object executes the outsourced task to obtain a local optimization result, and the local optimization result is sent to the cloud server; the outsourced objects include: the cloud server and the edge server with the remaining computing resources supporting the execution of the local optimization task.

[0132] Step S5023: Send the local optimization results to the cloud server, so that the cloud server performs the global optimization task based on the local optimization results sent by each edge server to obtain new global scheduling parameters. If the new global scheduling parameters do not meet the convergence conditions, the new global scheduling parameters are used as the initial global scheduling parameters.

[0133] Step S5024: Return to execute step S5021 until the cloud server determines that the new global scheduling parameters meet the convergence conditions and obtains the optimal global scheduling parameters.

[0134] It should be noted that, for the explanation of the embodiments of the present invention, please refer to the multi-microgrid group optimization scheduling method based on cloud-edge collaboration disclosed in the above-mentioned embodiment of the present invention, which will not be repeated here.

[0135] Based on the above-mentioned multi-microgrid group optimization scheduling method based on cloud-edge collaboration disclosed by the present invention, in this scheme, the layered structure of cloud-edge collaboration effectively reduces the amount of data transmission, improves the overall scheduling efficiency of the system, and only transmits scheduling parameters between the cloud server and each edge server, and sensitive information such as the specific load data and power generation data of the microgrid will not be obtained by the cloud layer, thereby ensuring data isolation and privacy security between various entities. By using encrypted outsourced computing technology, the problem of insufficient computing resources of edge servers is solved while ensuring data privacy, and the optimal configuration of computing resources is achieved. By using a distributed optimization algorithm, parallel solutions between the cloud server and each edge server are achieved, and the optimal global scheduling parameters are finally obtained through multiple iterations of interaction, which reduces the system cost while ensuring the overall economy and operation efficiency of the system.

[0136] like Figure 6 As shown, it is a structural diagram of a multi-microgrid cluster optimization scheduling device based on cloud-edge collaboration disclosed in an embodiment of the present invention, which is applied to a cloud server in a multi-microgrid cluster optimization scheduling system based on cloud-edge collaboration disclosed in the above embodiment of the present invention. The device includes: a receiving unit 601, a first initial scheduling unit 602 and a first iteration unit 603.

[0137] The receiving unit 601 is used to receive local scheduling parameters sent by each edge server; the local scheduling parameters are obtained by the edge server based on a pre-configured microgrid scheduling model, and the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid.

[0138] The first initial dispatching unit 602 is used to obtain initial global dispatching parameters based on various local dispatching parameters and a pre-configured distribution network dispatching model; the distribution network dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network.

[0139] The first iteration unit 603 is used to perform an interactive iteration process for each edge server based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0140] In one embodiment, the first iteration unit 603 is specifically configured to:

[0141] Send the initial global scheduling parameters to each edge server; receive the local optimization results obtained by each edge server performing the local optimization task based on the initial global scheduling parameters; perform the global optimization task based on each local optimization result to obtain a new global scheduling parameter; if the new global scheduling parameter does not meet the convergence condition, use the new global scheduling parameter as the initial global scheduling parameter, return to execute the step of sending the initial global scheduling parameter to each edge server, until the new global scheduling parameter meets the convergence condition, and obtain the optimal global scheduling parameter.

[0142] In one embodiment, the first iteration unit 603 for receiving the local optimization results obtained by each edge server performing the local optimization task based on the initial global scheduling parameters is specifically used to:

[0143] For each edge server whose remaining computing resources support executing local optimization tasks based on the initial global scheduling parameters, the local optimization results obtained by executing the local optimization tasks are received from the edge server; for the target edge server whose remaining computing resources do not support executing local optimization tasks based on the initial global scheduling parameters, the local optimization results sent by the outsourced computing object specified by the target edge server are received; the outsourced computing object obtains the local optimization results by receiving the outsourced tasks sent by the target edge server and executing the outsourced tasks; the outsourced tasks are obtained by encrypting and packaging the local optimization tasks by the target edge server using outsourcing encryption technology; the outsourced objects include: cloud servers and edge servers whose remaining computing resources support executing local optimization tasks.

[0144] In one embodiment, the device further comprises:

[0145] The initialization unit is used to obtain initial condition parameters before executing the interactive iteration process based on the initial global scheduling parameters and the distributed optimization algorithm for each edge server; and input the initial condition parameters into the distributed optimization algorithm for initialization setting.

[0146] Based on the above-mentioned multi-microgrid group optimization scheduling device based on cloud-edge collaboration disclosed by the present invention, in this scheme, the layered structure of cloud-edge collaboration effectively reduces the amount of data transmission, improves the overall scheduling efficiency of the system, and only transmits scheduling parameters between the cloud server and each edge server, and sensitive information such as the specific load data and power generation data of the microgrid will not be obtained by the cloud layer, thereby ensuring data isolation and privacy security between various entities. By using encrypted outsourced computing technology, the problem of insufficient computing resources of edge servers is solved while ensuring data privacy, and the optimal configuration of computing resources is achieved. By using distributed optimization algorithms, parallel solutions between cloud servers and edge servers are achieved, and through multiple iterations of interaction, the optimal global scheduling parameters are finally obtained, which reduces system costs while ensuring the overall economy and operation efficiency of the system.

[0147] like Figure 7 As shown, it is a structural diagram of another multi-microgrid cluster optimization scheduling device based on cloud-edge collaboration disclosed in an embodiment of the present invention, which is applied to an edge server in a multi-microgrid cluster optimization scheduling system based on cloud-edge collaboration disclosed in the above embodiment of the present invention. The device includes: a second initial scheduling unit 701 and a second iteration unit 702.

[0148] The second initial scheduling unit 701 is used to obtain local scheduling parameters based on a pre-configured microgrid scheduling model, and send the local scheduling parameters to the cloud server, so that the cloud server obtains the initial global scheduling parameters based on the local scheduling parameters sent by each edge server and the pre-configured distribution network scheduling model; the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; the distribution network scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network.

[0149] The second iteration unit 702 is used to perform an interactive iteration process based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

[0150] In one embodiment, the second iteration unit 702 is specifically configured to:

[0151] Receive the initial global scheduling parameters sent by the cloud server; perform local optimization tasks based on the initial global scheduling parameters to obtain local optimization results; send the local optimization results to the cloud server, so that the cloud server performs global optimization tasks based on the local optimization results sent by each edge server to obtain new global scheduling parameters. If the new global scheduling parameters do not meet the convergence conditions, the new global scheduling parameters are used as the initial global scheduling parameters; return to the step of receiving the initial global scheduling parameters sent by the cloud server until the cloud server determines that the new global scheduling parameters meet the convergence conditions and obtains the optimal global scheduling parameters.

[0152] In one embodiment, the second iterative unit 702 for performing the local optimization task based on the initial global scheduling parameters to obtain the local optimization result is specifically used to:

[0153] If the remaining computing resources support the execution of local optimization tasks based on the initial global scheduling parameters, the local optimization tasks are executed to obtain local optimization results; if the remaining computing resources do not support the execution of local optimization tasks based on the initial global scheduling parameters, the local optimization tasks are encrypted and packaged using outsourcing encryption technology to obtain outsourced tasks; the outsourced tasks are sent to any outsourced computing object, so that the outsourced computing object executes the outsourced tasks to obtain local optimization results, and the local optimization results are sent to the cloud server; the outsourced objects include: cloud servers and edge servers where the remaining computing resources support the execution of local optimization tasks.

[0154] Based on the above-mentioned multi-microgrid group optimization scheduling device based on cloud-edge collaboration disclosed by the present invention, in this scheme, the layered structure of cloud-edge collaboration effectively reduces the amount of data transmission, improves the overall scheduling efficiency of the system, and only transmits scheduling parameters between the cloud server and each edge server, and sensitive information such as the specific load data and power generation data of the microgrid will not be obtained by the cloud layer, thereby ensuring data isolation and privacy security between various entities. By using encrypted outsourced computing technology, the problem of insufficient computing resources of edge servers is solved while ensuring data privacy, and the optimal configuration of computing resources is achieved. By using distributed optimization algorithms, parallel solutions between cloud servers and edge servers are achieved, and through multiple iterations of interaction, the optimal global scheduling parameters are finally obtained, which reduces system costs while ensuring the overall economy and operation efficiency of the system.

[0155] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0156] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0157] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-microgrid cluster optimization scheduling method based on cloud-edge collaboration, characterized in that: A cloud server applied to a multi-microgrid cluster optimization dispatching system based on cloud-edge collaboration, wherein the cloud server is communicatively connected with a plurality of edge servers in the multi-microgrid cluster optimization dispatching system, the cloud server is deployed in a distribution network, and each edge server is deployed in a corresponding microgrid, and the method comprises: Receiving local scheduling parameters sent by each edge server; the local scheduling parameters are obtained by the edge server based on a pre-configured microgrid scheduling model, and the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; Based on each of the local dispatching parameters and a pre-configured distribution network dispatching model, an initial global dispatching parameter is obtained; the distribution network dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network; For each edge server, an interactive iteration process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

2. The method according to claim 1, characterized in that For each edge server, an interactive iteration process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained, including: Sending initial global scheduling parameters to each of the edge servers; Receiving local optimization results obtained by each edge server performing a local optimization task based on the initial global scheduling parameters; Performing a global optimization task based on each of the local optimization results to obtain a new global scheduling parameter; If the new global scheduling parameters do not meet the convergence conditions, the new global scheduling parameters are used as the initial global scheduling parameters, and the step of sending the initial global scheduling parameters to each edge server is returned to execute until the new global scheduling parameters meet the convergence conditions to obtain the optimal global scheduling parameters.

3. The method according to claim 2, characterized in that The receiving of local optimization results obtained by each edge server performing a local optimization task based on the initial global scheduling parameters includes: For each edge server whose remaining computing resources support executing the local optimization task based on the initial global scheduling parameters, receiving a local optimization result obtained by executing the local optimization task from the edge server; For a target edge server whose remaining computing resources do not support executing a local optimization task based on the initial global scheduling parameters, a local optimization result sent by an outsourced computing object specified by the target edge server is received; the outsourced computing object obtains the local optimization result by receiving an outsourced task sent by the target edge server and executing the outsourced task; the outsourced task is obtained by the target edge server by encrypting and packaging the local optimization task using outsourcing encryption technology; the outsourced object includes: the cloud server and the edge server whose remaining computing resources support executing the local optimization task.

4. The method according to claim 2, characterized in that: Before executing the interactive iteration process for each edge server based on the initial global scheduling parameters and the distributed optimization algorithm, the method further includes: Get initial condition parameters; The initial condition parameters are input into the distributed optimization algorithm for initialization setting.

5. A multi-microgrid cluster optimization scheduling method based on cloud-edge collaboration, characterized in that: Applied to any edge server in a multi-microgrid cluster optimization dispatching system based on cloud-edge collaboration, each edge server is communicatively connected with a cloud server in the multi-microgrid cluster optimization dispatching system, each edge server is deployed in a corresponding microgrid, and the cloud server is deployed in a distribution network, the method includes: Based on a pre-configured microgrid dispatching model, local dispatching parameters are obtained, and the local dispatching parameters are sent to the cloud server, so that the cloud server obtains initial global dispatching parameters based on the local dispatching parameters sent by each edge server and the pre-configured distribution network dispatching model; the microgrid dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; the distribution network dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network; An interactive iterative process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

6. The method according to claim 5, characterized in that The interactive iteration process is performed based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained, including: Receiving initial global scheduling parameters sent by the cloud server; Execute local optimization tasks based on the initial global scheduling parameters to obtain local optimization results; Sending the local optimization results to the cloud server, so that the cloud server performs the global optimization task based on the local optimization results sent by each edge server to obtain a new global scheduling parameter. If the new global scheduling parameter does not meet the convergence condition, the new global scheduling parameter is used as the initial global scheduling parameter; Return to the step of receiving the initial global scheduling parameters sent by the cloud server until the cloud server determines that the new global scheduling parameters meet the convergence condition and obtains the optimal global scheduling parameters.

7. The method according to claim 6, characterized in that The performing of the local optimization task based on the initial global scheduling parameters to obtain the local optimization result includes: If the remaining computing resources support executing the local optimization task based on the initial global scheduling parameters, then executing the local optimization task to obtain a local optimization result; If the remaining computing resources do not support the execution of the local optimization task based on the initial global scheduling parameters, the local optimization task is encrypted and packaged using the outsourcing encryption technology to obtain an outsourced task; The outsourced task is sent to any outsourced computing object, so that the outsourced computing object executes the outsourced task to obtain the local optimization result, and sends the local optimization result to the cloud server; the outsourced object includes: the cloud server and the edge server whose remaining computing resources support the execution of the local optimization task.

8. A multi-microgrid group optimization scheduling device based on cloud-edge collaboration, characterized in that: A cloud server applied to a multi-microgrid cluster optimization dispatching system based on cloud-edge collaboration, wherein the cloud server is communicatively connected with a plurality of edge servers in the multi-microgrid cluster optimization dispatching system, the cloud server is deployed in a distribution network, and each edge server is deployed in a corresponding microgrid, and the device comprises: A receiving unit, configured to receive local scheduling parameters sent by each edge server; the local scheduling parameters are obtained by the edge server based on a pre-configured microgrid scheduling model, and the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; A first initial dispatching unit, configured to obtain initial global dispatching parameters based on each of the local dispatching parameters and a pre-configured distribution network dispatching model; the distribution network dispatching model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network; The first iteration unit is used to perform an interactive iteration process for each edge server based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

9. A multi-microgrid group optimization scheduling device based on cloud-edge collaboration, characterized in that: Applied to any edge server in a multi-microgrid cluster optimization dispatching system based on cloud-edge collaboration, each edge server is communicatively connected with a cloud server in the multi-microgrid cluster optimization dispatching system, each edge server is deployed in a corresponding microgrid, and the cloud server is deployed in a distribution network, the device comprises: A second initial scheduling unit is used to obtain local scheduling parameters based on a pre-configured microgrid scheduling model, and send the local scheduling parameters to the cloud server, so that the cloud server obtains initial global scheduling parameters based on the local scheduling parameters sent by each edge server and the pre-configured distribution network scheduling model; the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; the distribution network scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network; The second iteration unit is used to perform an interactive iteration process based on the initial global scheduling parameters and the distributed optimization algorithm until the optimal global scheduling parameters are obtained.

10. A multi-microgrid cluster optimization scheduling system based on cloud-edge collaboration, characterized in that: The system comprises: a cloud server and a plurality of edge servers, wherein the cloud server is communicatively connected with a plurality of edge servers in the multi-microgrid group optimization dispatching system, the cloud server is deployed in a distribution network, and each of the edge servers is deployed in a corresponding microgrid; The cloud server is used to receive the local dispatch parameters sent by each edge server; based on each local dispatch parameter and a pre-configured distribution network dispatch model, an initial global dispatch parameter is obtained; the distribution network dispatch model is established based on an objective function with the goal of minimizing the total operating cost of the distribution network; for each edge server, an interactive iterative process is performed based on the initial global dispatch parameters and a distributed optimization algorithm until the optimal global dispatch parameter is obtained; The edge server is used to obtain local scheduling parameters based on a pre-configured microgrid scheduling model and send the local scheduling parameters to the cloud server; the microgrid scheduling model is established based on an objective function with the goal of minimizing the total operating cost of the microgrid; based on the initial global scheduling parameters and the distributed optimization algorithm, an interactive iterative process is performed until the optimal global scheduling parameters are obtained.

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