Power dispatching method, system and storage medium

Abstract

The application relates to a power dispatching method, a system and a storage medium. The power dispatching method is applied to a cloud master station and comprises the following steps: acquiring a current running state of a power node sent by an edge computing node, and updating a power grid database based on the current running state; dispatching power grid data from the updated power grid database and allocating a static security analysis calculation task to the edge computing node according to a computing resource and a storage resource, so that the edge computing node performs static security analysis calculation on the power grid data under the condition that the static security analysis calculation task is received, obtains a calculation result and uploads the calculation result to the cloud master station; receiving the calculation result sent by the edge computing node; based on the calculation result, a power system dispatching plan is formulated to meet the balance between power production and power consumption in a small time scale, and the power system dispatching plan is output to the edge computing node, so that the edge computing node controls a control device in the power node to execute the power system dispatching plan. The power dispatching method has high reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system automatic dispatching, in particular to a power dispatching method and system and a storage medium. BACKGROUND

[0002] With more and more new energy power sources connected to the power grid, the proportion of new energy power sources in the capacity of the power grid is increasing. On the other hand, the connection of a large number of power electronic devices also reduces the "inertia" of the operation of the power system. The power system faces more complex operating conditions and application requirements, and needs to process and maintain information with smaller and smaller time granularity, and the amount of information also increases explosively. Therefore, in recent years, the State Grid has established the key strategic goal of intelligent and digital transformation, aiming to use the computer technology, communication technology and intelligent technology that have made considerable progress at present, apply modern information data processing technology and advanced communication technology in the power system, cope with the challenges of the current complex power grid, and improve the efficiency and reliability of the overall operation of the power system.

[0003] In the implementation process, the inventors found that the existing power dispatching method has at least the following problem: the reliability of the existing power dispatching method is not high. SUMMARY

[0004] Therefore, it is necessary to provide a power dispatching method, system and storage medium aiming at the above technical problems.

[0005] A power dispatching method applied to a cloud master station, comprising:

[0006] obtaining a current operating state of a power node sent by an edge computing node, and updating a power grid database based on the current operating state;

[0007] scheduling power grid data from the updated power grid database and allocating a static security analysis calculation task to the edge computing node according to a computing resource and a storage resource, so that the edge computing node performs static security analysis calculation on the power grid data in the case of receiving the static security analysis calculation task, obtains a calculation result, and uploads the calculation result to the cloud master station;

[0008] receiving the calculation result sent by the edge computing node;

[0009] based on the calculation result, formulating a power system dispatching plan to meet the balance between power production and power consumption in a small time scale, and outputting the power system dispatching plan to the edge computing node, so that the edge computing node controls a control device in the power node to execute the power system dispatching plan.

[0010] In one embodiment, the step of obtaining the current operating state of the power node sent by the edge computing node and updating the power grid database based on the current operating state comprises:

[0011] The auxiliary decision information and the current running state are acquired, and the auxiliary decision information is obtained by the edge computing node according to the current running state and the historical running state of the power node, and the power output and the load running state of the node in a small time scale are predicted.

[0012] The power grid database is updated based on the current running state and the auxiliary decision information.

[0013] In one of the embodiments, the step of scheduling power grid data from the updated power grid database and allocating a static security analysis calculation task to the edge computing node according to the computing resource and the storage resource comprises:

[0014] The analysis data is sent to the edge computing node, and the power grid data is scheduled from the updated power grid database and the static security analysis calculation task is allocated to the edge computing node according to the computing resource and the storage resource; the analysis data comprises the calculation parameter and the convergence criterion.

[0015] In one of the embodiments, the power system dispatching plan comprises the output plan of the power source body in a small time scale and the power consumption plan of the load body in a small time scale.

[0016] Based on the calculation result, the step of formulating the power system dispatching plan with the goal of meeting the balance between power production and power consumption in a small time scale comprises:

[0017] Based on the calculation result, the step of formulating the power system dispatching plan with the goal of meeting the balance between power production and power consumption in a small time scale under the condition of meeting the constraint condition comprises:

[0018] In one of the embodiments, after the step of formulating the power system dispatching plan with the goal of meeting the balance between power production and power consumption in a small time scale based on the calculation result and outputting to the edge computing node, the method further comprises:

[0019] The running state of the power node after the control device executes the power system dispatching plan is received.

[0020] In the case of receiving the dispatching request, the power system dispatching plan is adjusted based on the running state of the power node after the control device executes the power system dispatching plan.

[0021] The adjusted power system dispatching plan is sent to the edge computing node.

[0022] A power dispatching method applied to an edge computing node, comprising:

[0023] The current operation state of the power node is collected and sent to the cloud master station, so that the cloud master station updates the power grid database based on the current operation state, and allocates the static security analysis calculation task to the edge computing node from the updated power grid database according to the computing resources and storage resources.

[0024] The power grid data and the static security analysis calculation task sent by the cloud master station are received, and in the case of receiving the static security analysis calculation task, the static security analysis calculation is performed on the power grid data to obtain the calculation result and upload to the cloud master station.

[0025] The power system dispatching plan sent by the cloud master station is received, and the control equipment in the power node is controlled to execute the power system dispatching plan; the power system dispatching plan is formulated by the cloud master station according to the calculation result, with the goal of meeting the balance between power production and power consumption in a small time scale.

[0026] In one embodiment, the step of collecting the current operation state of the power node and sending it to the cloud master station comprises:

[0027] The current operation state is collected, and the node power output and load operation in a small time scale are predicted according to the current operation state and the historical operation state of the power node to obtain auxiliary decision information.

[0028] The current operation state and the auxiliary decision information are sent to the cloud master station.

[0029] In one embodiment, after the step of receiving the power system dispatching plan sent by the cloud master station and controlling the control equipment in the power node to execute the power system dispatching plan, it further comprises:

[0030] The operation state of the power node after controlling the control equipment to execute the power system dispatching plan is collected and sent to the cloud master station, and a dispatching request is sent to the cloud master station; the dispatching request is used to instruct the cloud master station to adjust the power system dispatching plan based on the operation state of the power node after controlling the control equipment to execute the power system dispatching plan.

[0031] The adjusted power system dispatching plan sent by the cloud master station is received, and the control equipment in the power node is controlled to execute the adjusted power system dispatching plan.

[0032] A power dispatching system comprises a cloud master station and an edge computing node connected to the cloud master station;

[0033] The cloud master station is used to execute the above-mentioned power dispatching method;

[0034] The edge computing node is used to execute the above-mentioned power dispatching method.

[0035] A computer readable storage medium, having stored thereon a computer program, the computer program being executed by a processor to implement the steps of the method.

[0036] The above technical solution has at least the following advantages and beneficial effects:

[0037] The application constructs a small time scale power dispatching method based on cloud edge cooperation, monitors the real-time operation status of the power node through the edge computing node, uploads the node information, provides support for the dispatching decision of the cloud master station, the cloud master station receives and combines the node information provided by the edge computing node, maintains the power grid database resource, and accurately and reliably dispatches the power in a small time scale, the edge computing node receives and executes the power dispatching plan of the cloud master station, and the cloud master station uniformly monitors and dispatches the computer resources of the whole cloud edge system, and reasonably allocates the calculation tasks according to the load state and network condition of each calculation host. The response capability to the real-time information of the power system operation is improved, the accurate and reliable power dispatching of the small time scale of the power system is realized, so that the application can adapt to the characteristics of the new power system, such as large number of power load main bodies, randomness and large fluctuation, improve new energy consumption, improve the accuracy and reliability of the power dispatching method, and effectively improve the overall safety level of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0039] Figure 1 A flowchart of a power dispatching method applied to a cloud master station in an embodiment;

[0040] Figure 2 A flowchart of the step of obtaining the current running state of the power node sent by the edge computing node and updating the power grid database based on the current running state in an embodiment;

[0041] Figure 3 A flowchart of the step of formulating a power system dispatching plan and outputting to the edge computing node in an embodiment;

[0042] Figure 4 A flowchart of a power dispatching method applied to an edge computing node in an embodiment;

[0043] Figure 5A flowchart of a step of collecting the current operating state of the power node and sending to the cloud master station in an embodiment;

[0044] Figure 6 A flowchart of a step of controlling the control device in the power node to execute the power system dispatching plan in an embodiment;

[0045] Figure 7 A structural block diagram of the power dispatching device applied to the cloud master station in an embodiment;

[0046] Figure 8 A structural block diagram of the power dispatching device applied to the edge computing node in an embodiment;

[0047] Figure 9 A structural block diagram of the power dispatching system in an embodiment;

[0048] Figure 10 A structural block diagram of the power dispatching system in another embodiment. DETAILED DESCRIPTION

[0049] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments of the present application and are not intended to limit the present application.

[0051] It can be understood that the terms "first", "second", and the like used in the present application can be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from another element.

[0052] It should be noted that when an element is considered to be "connected" to another element, it can be directly connected to another element or connected to another element through a central element. In addition, "connected" in the following embodiments should be understood as "electrically connected", "communicatively connected" and the like if there is transmission of electrical signals or data between the connected objects.

[0053] As used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. It will be further understood that the terms "comprises", "comprising", "includes" and / or "including", or the like, when used in this specification, specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof.

[0054] In one embodiment, as shown in Figure 1 A power scheduling method is provided, which is applied to a cloud master station and can include:

[0055] In step 202, the current running state of the power node sent by the edge computing node is obtained, and the power grid database is updated based on the current running state;

[0056] In step 204, the power grid data is scheduled and the static security analysis calculation task is allocated to the edge computing node from the updated power grid database according to the computing resource and the storage resource, so that the edge computing node performs static security analysis calculation on the power grid data in the case of receiving the static security analysis calculation task, obtains the calculation result and uploads it to the cloud master station;

[0057] In step 206, the calculation result sent by the edge computing node is received;

[0058] In step 208, based on the calculation result, a power system scheduling plan is formulated to meet the balance between power production and power consumption in a small time scale, and is output to the edge computing node, so that the edge computing node controls the control equipment in the power node to execute the power system scheduling plan.

[0059] The power scheduling method can be applied to the cloud master station, the cloud master station communicates with the edge computing node through the network, the power grid database can be stored in the cloud master station or placed on other network servers, and the server can be realized by an independent server or a server cluster composed of multiple servers. The edge computing node of the application is configured in each subject participating in power scheduling, including power plants, industrial loads, residential loads, new energy stations, etc.; the edge cluster and each subject participating in power scheduling communicate and interact through the edge gateway; the current running state of the power node includes the connection relationship between the node and the power grid, the power supply parameters and models (traditional generators and new energy), the power load model, the transformer model, the line impedance parameters and the node power flow data, etc.; the control equipment in the power node is the control equipment in the power node.

[0060] As one of the key technologies of Internet of Things applications, edge computing can effectively improve system operation efficiency by providing data services at the edge of the system through the integration of network, storage, computing and other technologies. The objects of edge computing operation include downlink data from cloud services and uplink data from Internet of Things services. In the application of power system, edge computing is beneficial to the solution of problems such as heterogeneous connection of massive data in power grid information management system, real-time and intelligence of business, and can optimize the cloud server, plan the computing and storage resources, and greatly improve the real-time response of business under the condition of efficient use of communication resources. Therefore, by introducing the deep integration of edge computing and power system operation control in multiple directions, the real-time response of complex demand under massive data is realized, and the intelligent construction of power grid is comprehensively promoted.

[0061] Specifically, the edge computing node of the present application monitors the current running state of the power node in real time, the cloud master station updates the power grid database according to the current running state sent by the edge computing node, and monitors the computing and storage resources of the whole cloud-edge system, and according to the computing resources and storage resources, the power grid data in the updated power grid database is uniformly scheduled to the edge computing node, and the static security analysis calculation task is allocated to the edge computing node, so that the edge computing node performs static security analysis calculation on the received power grid data under the condition of receiving the static security analysis calculation task, and uploads the calculation result to the cloud master station; wherein, the static security analysis is to disconnect each fault-free line, transformer, generator, bus and other single elements or their predefined combination, to analyze whether other elements are overloaded and whether the power grid voltage level meets the requirements, in order to check whether the cross-section structure strength and operation mode meet the requirements of safe operation; the static security analysis algorithm is formulated by the cloud master station and pre-built in the edge computing node. After receiving the calculation result sent by the edge computing node, the cloud master station formulates the power system dispatching plan based on the calculation result and outputs it to the edge computing node, wherein the day-ahead dispatching in power dispatching takes hours as the granularity, and the small time scale is the dispatching less than the hour time period; forming the power system dispatching plan means arranging the power distribution of each power node in the power system, which is usually obtained according to the prediction algorithm combined with the static security analysis calculation result. The edge computing node controls the control equipment in the power node to execute the power system dispatching plan under the condition of receiving the power system dispatching plan sent by the cloud master station.

[0062] The application constructs a small time scale power dispatching method based on cloud edge cooperation. In view of the current situation that there are many power line power sources and load nodes, the operation is complex, and new energy needs to be quickly consumed, the cloud-edge technology is combined with the automatic dispatching of the power grid, the operation of the power grid is monitored in real time through the edge computing node, and the cloud master station dispatches the power grid data and static security analysis calculation tasks to the edge computing node according to the computing resources and storage resources, through the cooperation between the cloud master station and the edge computing node, the collected information is quickly and timely analyzed and processed, the effect of small time scale dispatching of the power system is realized, and the response level of the power grid to complex working conditions is improved, thereby further enhancing the safe and stable operation level of the power grid.

[0063] In one embodiment, as shown in Figure 2 The step 202 of obtaining the current operation state of the power node sent by the edge computing node and updating the power grid database based on the current operation state can include:

[0064] Step 302, obtaining the auxiliary decision information and the current operation state sent by the edge computing node; the auxiliary decision information is obtained by the edge computing node according to the current operation state and the historical operation state of the power node, and the node power output and the load operation in the small time scale are predicted;

[0065] Step 304, updating the power grid database based on the current operation state and the auxiliary decision information.

[0066] Specifically, the edge computing node also collects the current operation state of the power node, and according to the current operation state of the power node and the historical operation state of the power node, the node power output and the load operation in the small time scale are predicted to obtain the auxiliary decision information; wherein the historical operation state of the power node includes the past data corresponding to the real-time information of the node and the power grid connection relationship, power supply parameters and models (traditional generator and new energy), power load model, transformer model, line impedance parameter and node power flow data, that is, the dispatching plan of the power system depends on load prediction, prediction technology depends on linear regression, and linear regression depends on historical data such as past node power flow and parameters. The edge computing node sends the current operation state of the power node and the calculated auxiliary decision information to the cloud master station.

[0067] The cloud master station updates the power grid database based on the current operation state of the power node and the auxiliary decision information sent by the edge computing node, thereby laying a foundation for power dispatching decision, which is helpful to realize the accurate and reliable small time scale power dispatching of the cloud master station.

[0068] In one of the embodiments, the step 204 of scheduling power grid data from the updated power grid database and allocating static security analysis calculation tasks to the edge computing node according to the computing resources and the storage resources can include:

[0069] The analysis data can include the calculation parameters and the convergence criterion.

[0070] Specifically, the cloud master station also delivers the analysis data required for the static security analysis calculation, such as the calculation parameters and the convergence criterion, to the edge computing node at the same time of scheduling the power grid data from the updated power grid database and allocating the static security analysis calculation tasks to the edge computing node according to the computing resources and the storage resources. The calculation parameters can include the line, transformer, generator, bus, and other parameters and line connection information, and the convergence criterion is required because the static security analysis calculation is an iterative algorithm. The edge computing node can perform the static security analysis calculation based on the received analysis data and power grid data, thereby effectively improving the accuracy of the static security analysis calculation performed by the edge computing node, and further improving the accuracy and reliability of the power system dispatching plan formed by the cloud master station in a small time scale.

[0071] In one of the embodiments, the power system dispatching plan can include the power output plan of the power source subject and the power consumption plan of the load subject in a small time scale.

[0072] The step 208 of formulating the power system dispatching plan based on the calculation results to meet the balance between power production and power consumption in a small time scale can include:

[0073] The step 208 of formulating the power system dispatching plan based on the calculation results to meet the balance between power production and power consumption in a small time scale under the condition of meeting the constraint conditions. The constraint conditions include the grid frequency requirement, the voltage requirement, and the line loss requirement.

[0074] Specifically, the cloud master station formulates the power system dispatching plan in a small time scale to meet the balance between power production and power consumption under the condition of meeting the grid frequency requirement, the voltage requirement, and the line loss requirement, and finally sends the power system dispatching plan to the edge computing node to make the edge computing node control the control equipment in the power node to execute the power system dispatching plan, thereby effectively realizing the accurate and reliable power dispatching of the power system in a small time scale, better adapting to the characteristics of the large number of power source and load subjects in the new power system, the randomness and volatility, improving the new energy consumption, and further improving the overall safety level of the power system.

[0075] In one of the embodiments, as shown in Figure 3 After the step 208 of formulating the power system dispatching plan and outputting to the edge computing node based on the calculation results to meet the balance between power production and power consumption in a small time scale, the step can further include:

[0076] Step 402, receiving the dispatching request sent by the edge computing node and the running state of the power node after the control control device executes the power system dispatching plan;

[0077] Step 404, in the case of receiving the dispatching request, adjusting the power system dispatching plan based on the running state of the power node after the control control device executes the power system dispatching plan;

[0078] Step 406, sending the adjusted power system dispatching plan to the edge computing node.

[0079] Specifically, after the edge computing node starts to execute the power system dispatching plan, it will continue to obtain the running state of the power node after executing the power system dispatching plan in real time, and again according to the obtained running state of the power node and the historical running state of the power node, the power output and load running state of the node in a small time scale are predicted to obtain the auxiliary decision information and sent to the cloud master station, at the same time, the edge computing node sends a dispatching request to the cloud master station.

[0080] The cloud master station adjusts the power system dispatching plan based on the operation state of the power node after the control device executes the power system dispatching plan, that is, the cloud master station updates the power grid database based on the operation state of the power node after the control device executes the power system dispatching plan and the auxiliary decision information, and then dispatches the power grid data from the updated power grid database and allocates the static security analysis calculation task to the edge computing node again according to the computing resources and storage resources, so that the edge computing node performs static security analysis calculation on the power grid data when receiving the static security analysis calculation task, obtains the calculation result and uploads it to the cloud master station, so that the cloud master station again formulates the power system dispatching plan based on the calculation result to meet the balance between power generation and power consumption in a small time scale, and outputs it to the edge computing node to control the control device in the power node to execute the adjusted power system dispatching plan. Through the continuous interaction of information between the cloud master station and the edge computing node, a real-time interaction and mutual influence process is completed, the power system dispatching plan is continuously adjusted according to the real-time state of each subject participating in power dispatching, and the control device is controlled to operate according to the adjusted power system dispatching plan, thereby realizing accurate and reliable small time scale dispatching of the power system and ensuring safe and stable operation of the power system.

[0081] The above, the application provides a kind of based on cloud edge cooperation Small time scale power dispatching method, for current power line power supply and load node numerous, running situation is complex, new energy needs fast consumption status, by edge computing node monitoring the real-time running condition of power node, send node information, timely analyze the running trend of node in small time scale, support the dispatching decision of cloud master station, receive and execute the power system dispatching plan of cloud master station;Cloud master station receives and converges the current operation state of power node and auxiliary decision information provided by edge computing node, maintains power system operation database resources, and uniformly monitors and dispatches the computer resources of entire cloud edge system, according to the load state and network condition of each computing host, task is reasonably distributed, and according to the static security analysis calculation result of edge computing node feedback, accurately and reliably dispatches small time scale power.The application effectively improves the response capability of real-time information of power system operation, realizes accurate and reliable small time scale power dispatching of power system, adapts to the characteristics of large number of power supply and load subjects in new power system, large randomness and volatility, effectively improves new energy consumption, and further improves the overall safety level of power system.

[0082] In one embodiment, as shown in FIG. Figure 4 A power dispatching method applied to an edge computing node can include:

[0083] In step 502, the current operating state of the power node is collected and sent to the cloud master station, so that the cloud master station updates the power grid database based on the current operating state, and according to the computing resources and storage resources, dispatches the power grid data from the updated power grid database and allocates the static security analysis calculation task to the edge computing node.

[0084] In step 504, the power grid data and the static security analysis calculation task sent by the cloud master station are received, and in the case that the static security analysis calculation task is received, the power grid data is subjected to static security analysis calculation, and the calculation result is uploaded to the cloud master station.

[0085] In step 506, the power system dispatching plan sent by the cloud master station is received, and the control equipment in the power node is controlled to execute the power system dispatching plan. The power system dispatching plan is formulated by the cloud master station according to the calculation result, with the goal of meeting the balance between power production and power consumption in a small time scale.

[0086] Specifically, the edge computing node communicates with each subject participating in power dispatching, collects the current operating state of the power node in real time, including the connection relationship with the power grid, power supply parameters and models (traditional generators and new energy), power load models, transformer models, line impedance parameters, node power flow data, etc., and sends the collected current operating state of the power node to the cloud master station, so that the cloud master station updates the power grid database based on the received current operating state of the power node, and through monitoring the computing and storage resources of the entire cloud-edge system, according to the computing resources and storage resources, dispatches the power grid data in the updated power grid database to the edge computing node, and allocates the static security analysis calculation task to the edge computing node.

[0087] When an edge computing node receives a static security analysis calculation task from the cloud master station, it performs static security analysis calculations on the received power grid data and uploads the results to the cloud master station. The static security analysis involves disconnecting individual components such as lines, transformers, generators, and buses, or their predefined combinations, without faults, to analyze whether other components are overloaded and whether the grid voltage level meets requirements. This verifies whether the cross-sectional structural strength and operating mode meet safe operation requirements. The static security analysis algorithm is formulated by the cloud master station and pre-built into the edge computing node. Upon receiving the calculation results from the edge computing node, the cloud master station, based on the results, formulates a power system dispatch plan with the goal of achieving a balance between power production and power consumption within a small time scale and outputs it to the edge computing node. In power dispatch, day-ahead dispatch is granular at the hour level, and a small time scale refers to dispatch within time periods less than an hour. The resulting power system dispatch plan arranges the power allocation among various power nodes within the power system, typically obtained based on a prediction algorithm combined with the static security analysis calculation results. When the edge computing node receives the power system scheduling plan sent by the cloud master station, it controls the control equipment within the power node to execute the power system scheduling plan.

[0088] This application provides data services to the edge of the power system by setting up edge computing nodes, which can effectively improve the system's operating efficiency. The objects of edge computing operations include downlink data from cloud services and uplink data from Internet of Things services. In power system applications, edge computing is beneficial for solving problems such as heterogeneous connection of massive data, real-time business, and intelligence in power grid information management systems. Collaborative optimization with cloud servers and overall planning of computing and storage resources can greatly improve the real-time response of business while making efficient use of communication resources. Through real-time data interaction between the cloud master station and edge computing nodes, this application combines cloud-edge technology with power grid automated dispatching, enabling real-time monitoring of power grid operation and rapid and timely analysis and processing of collected information. This achieves small-scale dispatching of the power system, improves the power grid's response to complex operating conditions, and enhances the accuracy and reliability of power dispatching, further strengthening the safe and stable operation of the power grid.

[0089] In one embodiment, such as Figure 5 As shown, step 502, which involves collecting the current operating status of power nodes and sending it to the cloud master station, may include:

[0090] Step 602: Collect the current operating status, and based on the current operating status and the historical operating status of the power nodes, predict the node power output and load operation within a small time scale to obtain auxiliary decision-making information;

[0091] Step 604: Send the current operating status and auxiliary decision-making information to the cloud main station.

[0092] Specifically, after collecting the current operating status of power nodes, edge computing nodes also predict the node power output and load operation within a small time scale based on the current and historical operating status of the power nodes to obtain auxiliary decision-making information. The historical operating status of the power nodes includes past data corresponding to real-time information such as "the node's connection relationship with the grid, power supply parameters and models (traditional generators and new energy sources), power load models, transformer models, line impedance parameters, and node power flow data." In other words, the power system's scheduling plan relies on load forecasting, the forecasting technique relies on linear regression, and linear regression relies on historical data such as past node power flow and parameters. The edge computing nodes then send the monitored current operating status of the power nodes and the calculated auxiliary decision-making information to the cloud master station. This allows the cloud master station to receive the current operating status and auxiliary decision-making information of the power nodes sent by the edge computing nodes. Based on the current operating status and auxiliary decision-making information of the power nodes, it updates the power grid database. According to computing and storage resources, it schedules power grid data from the updated power grid database and allocates static security analysis calculation tasks to the edge computing nodes. At the same time, it also sends the analysis data required for static security analysis calculation to the edge computing nodes, such as the calculation parameters and convergence criteria. The calculation parameters may include parameters of lines, transformers, generators, buses, etc., as well as line connection information.

[0093] Edge computing nodes can perform static security analysis calculations based on received analytical data and power grid data, and send the calculation results to the cloud master station. Upon receiving the calculation results from the edge computing nodes, the cloud master station, under constraints such as power grid frequency requirements, voltage requirements, and line loss requirements, aims to achieve a balance between power production and consumption within a small timescale, ultimately forming a small-scale power system dispatch plan and concurrently sending it to the edge computing nodes. The edge computing nodes, upon receiving the power system dispatch plan, control the control equipment within their respective power nodes to execute the plan. Therefore, this application effectively improves the accuracy of static security analysis calculations performed by edge computing nodes, thereby improving the accuracy and reliability of the small-scale power system dispatch plan formed by the cloud master station.

[0094] In one embodiment, such as Figure 6 As shown, after step 506, which involves receiving the power system dispatch plan sent by the cloud master station and controlling the control equipment within the power node to execute the power system dispatch plan, the process may further include:

[0095] At step 702, the operation state of the power node after the control device executes the power system scheduling plan is sent to the cloud master station, and a scheduling request is sent to the cloud master station; the scheduling request is used to instruct the cloud master station to adjust the power system scheduling plan based on the operation state of the power node after the control device executes the power system scheduling plan;

[0096] At step 704, the adjusted power system scheduling plan sent by the cloud master station is received, and the control device in the power node is controlled to execute the adjusted power system scheduling plan.

[0097] Specifically, after the edge computing node receives the power system scheduling plan sent by the cloud master station and controls the control device in the power node to execute the power system scheduling plan, it will continue to collect the operation state of the power node after executing the power system scheduling plan in real time, and again predict the node power output and load operation in a short time scale according to the obtained operation state of the power node and the historical operation state of the power node to obtain auxiliary decision information sent to the cloud master station, and the edge computing node sends a scheduling request to the cloud master station, so that the cloud master station receives the scheduling request sent by the edge computing node, the operation state of the power node after the control device executes the power system scheduling plan, and the auxiliary decision information, and adjusts the power system scheduling plan based on the operation state of the power node after the control device executes the power system scheduling plan, that is, the cloud master station updates the power grid database based on the operation state of the power node after the control device executes the power system scheduling plan and the auxiliary decision information, and again schedules the power grid data from the updated power grid database and allocates a static security analysis calculation task to the edge computing node according to the computing resources and storage resources.

[0098] The edge computing node then performs static security analysis calculation on the power grid data based on the received static security analysis calculation task, obtains the calculation result and uploads it to the cloud master station, so that the cloud master station again formulates a power system scheduling plan based on the latest calculation result uploaded by the edge computing node to meet the balance between power production and power consumption in a short time scale, and outputs it to the edge computing node. The edge computing node then controls the control device in the power node to execute the adjusted power system scheduling plan sent by the cloud master station again.

[0099] Through the real-time monitoring of the edge computing node on the running state of the power node, the cloud master station adjusting the power system scheduling plan based on the real-time running state of the power node, and the real-time information interaction between the edge computing node and the cloud master station, the mutual influence process between the cloud edge system is realized, so that the power system scheduling plan is continuously adjusted according to the real-time state of each subject participating in power dispatching, and the equipment operation is controlled according to the adjusted power system scheduling plan, thereby realizing the accurate and reliable scheduling of the power system in a short time scale, and ensuring the safe and stable operation of the power system.

[0100] It should be understood that, although Figures 1-6 The steps in the flowchart of the application are displayed in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figures 1-6 At least part of the steps in the flowchart of the application can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0101] In one embodiment, as shown in Figure 7 A power dispatching device is provided, which is applied to a cloud master station and can include:

[0102] The data acquisition module 110 is configured to acquire the current running state of the power node sent by the edge computing node, and update the power grid database based on the current running state;

[0103] The processing module 120 is configured to dispatch power grid data from the updated power grid database and allocate a static security analysis calculation task to the edge computing node according to the computing resource and the storage resource, so that the edge computing node performs static security analysis calculation on the power grid data in the case of receiving the static security analysis calculation task, obtains a calculation result and uploads it to the cloud master station;

[0104] The first receiving module 130 is configured to receive the calculation result sent by the edge computing node;

[0105] The first planning module 140 is configured to formulate a power system scheduling plan based on the calculation result, with the goal of meeting the balance between power production and power consumption in a short time scale, and output it to the edge computing node, so that the edge computing node controls the control equipment in the power node to execute the power system scheduling plan.

[0106] In one of the embodiments, the data acquisition module 110 is further configured to acquire the auxiliary decision information and the current running state sent by the edge computing node; the auxiliary decision information is obtained by the edge computing node based on the current running state and the historical running state of the power node, and is used for predicting the power output and the load running state of the node in a short time scale; and the power grid database is updated based on the current running state and the auxiliary decision information.

[0107] In one of the embodiments, the processing module 120 is further configured to send the analysis data to the edge computing node, and schedule the power grid data from the updated power grid database and allocate the static security analysis calculation task to the edge computing node based on the computing resource and the storage resource; the analysis data includes the calculation parameter and the convergence criterion.

[0108] In one of the embodiments, the power system dispatching plan can include the output plan of the power source body in a short time scale and the electricity consumption plan of the load body in a short time scale.

[0109] The first planning module 140 is further configured to, based on the calculation result, formulate the power system dispatching plan in the case of meeting the constraint condition, and taking the balance between the power production and the power consumption in a short time scale as the target; the constraint condition includes the grid frequency requirement, the voltage requirement and the line loss requirement.

[0110] In one of the embodiments, the power dispatching device can further include:

[0111] The second receiving module is configured to receive the dispatching request sent by the edge computing node and the running state of the power node after the control control device executes the power system dispatching plan;

[0112] The second planning module is configured to, in the case of receiving the dispatching request, adjust the power system dispatching plan based on the running state of the power node after the control control device executes the power system dispatching plan;

[0113] The data sending module is configured to send the adjusted power system dispatching plan to the edge computing node.

[0114] In one of the embodiments, as shown in Figure 8 a power dispatching device applied to an edge computing node can include:

[0115] The first data acquisition module 210 is configured to acquire the current running state of the power node and send it to the cloud master station, so that the cloud master station updates the power grid database based on the current running state in the case of acquiring the current running state, and schedules the power grid data from the updated power grid database and allocates the static security analysis calculation task to the edge computing node based on the computing resource and the storage resource;

[0116] The data analysis module 220 is configured to receive power grid data and a static security analysis calculation task sent by the cloud master station, and perform static security analysis calculation on the power grid data when the static security analysis calculation task is received, to obtain a calculation result and upload the calculation result to the cloud master station.

[0117] The first control module 230 is configured to receive a power system dispatching plan sent by the cloud master station, and control a control device in the power node to execute the power system dispatching plan. The power system dispatching plan is formulated by the cloud master station based on the calculation result, and aims to balance power production and power consumption in a short time scale.

[0118] In one of the embodiments, the first data collection module 210 is further configured to collect a current operating state, and predict a node power output condition and a load operating condition in a short time scale according to the current operating state and a historical operating state of the power node, to obtain auxiliary decision information. The current operating state and the auxiliary decision information are sent to the cloud master station.

[0119] In one of the embodiments, the power dispatching device can further include:

[0120] The second data collection module is configured to collect an operating state of the power node after the control device executes the power system dispatching plan, and send the operating state to the cloud master station, and send a dispatching request to the cloud master station. The dispatching request is used to instruct the cloud master station to adjust the power system dispatching plan based on the operating state of the power node after the control device executes the power system dispatching plan.

[0121] The second control module is configured to receive an adjusted power system dispatching plan sent by the cloud master station, and control the control device in the power node to execute the adjusted power system dispatching plan.

[0122] The specific limitations of the power dispatching device can be referred to the limitations of the power dispatching method in the above, which will not be repeated here. The modules in the above power dispatching device can be realized by software, hardware and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules. It should be noted that the division of the modules in the embodiments of the present application is illustrative, and is only a logical function division. In actual implementation, there can be another division manner.

[0123] In one embodiment, as shown in Figure 9 , a power dispatching system is provided, which can include a cloud master station and an edge computing node connected to the cloud master station;

[0124] The cloud master station is configured to perform the power dispatching method described above;

[0125] The edge computing node is configured to perform the power dispatch method.

[0126] In one embodiment, as shown in Figure 10 The power dispatch system can further include an edge gateway connected to the edge computing node.

[0127] The edge gateway is further configured to connect to a control device.

[0128] Specifically, the power dispatch system of the present application configures edge computing nodes for various subjects participating in power dispatch, including power plants, industrial loads, residential loads, new energy stations, etc. The edge cluster communicates and interacts with various subjects participating in power dispatch through the edge gateway. The real-time operating conditions of the power nodes are monitored through the edge computing node, and the node information is uploaded. The running trend of the node in a short time scale is analyzed in a timely manner to support the dispatch decision of the cloud master station. The power dispatch decision of the cloud master station is received and executed. The cloud master station receives and combines the node information and auxiliary decision information provided by the edge computing node, maintains the power system operation database resources, and accurately and reliably dispatches the power in a short time scale. On the other hand, the cloud master station uniformly monitors and dispatches the computer resources of the whole cloud edge system, and reasonably allocates tasks according to the load state and network condition of each computing host. The present application can improve the response capability to the real-time information of the power system operation, realize accurate and reliable power dispatch of the power system in a short time scale, adapt to the characteristics of large number of power source and load subjects, large randomness and volatility in the new type of power system, improve new energy consumption, and improve the overall safety level of the power system.

[0129] In summary, the present application provides a power dispatch system based on cloud-edge collaboration. In view of the current situation that there are many power line power sources and load nodes, the operation is complex, and the new energy needs to be quickly consumed, the cloud-edge technology is combined with the power grid automatic dispatch to realize real-time monitoring of the power grid operation, and the collected information is quickly and timely analyzed and processed to realize short time scale dispatch of the power system. The response level of the power grid to complex working conditions and the accuracy and reliability of power dispatch are effectively improved, and the safety and stability of the power grid are further enhanced.

[0130] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0131] In one embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0132] In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0134] In the description of the present specification, the description of the terms "some embodiments", "other embodiments", "ideal embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are contained in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example.

[0135] Any combination of the technical features in the above embodiments can be made, and for the sake of brevity, not all possible combinations are described above, however, as long as the combination of the technical features does not exist in contradiction, it shall be considered within the scope of the present disclosure.

[0136] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it shall not be understood as a limitation on the patent scope of the present application. It shall be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these shall be within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A power dispatching method, characterized by, Applied to a cloud master station, comprising: obtaining the current running state and auxiliary decision information of the power node sent by the edge computing node, and updating the power grid database based on the current running state and the auxiliary decision information; the edge computing node is at least configured in a power plant, an industrial load, a residential load and a new energy station; the auxiliary decision information is obtained by the edge computing node according to the current running state and the historical running state of the power node; the running state of the power node at least includes the connection relationship between the power node and the power grid, the power supply parameter and the model, the power load model, the transformer model, the line impedance parameter and the power flow data of the power node; According to the computing resources and storage resources, the power grid data is dispatched from the updated power grid database, and the static security analysis calculation task is allocated to the edge computing node, so that the edge computing node performs static security analysis calculation on the power grid data when receiving the static security analysis calculation task, and uploads the calculation result to the cloud master station; Receiving the calculation result sent by the edge computing node; Based on the calculation result, under the constraint of the constraint condition, the balance between power production and power consumption in a small time scale is satisfied as the target, the power system dispatching plan is made and output to the edge computing node, so that the edge computing node controls the control equipment in the power node to execute the power system dispatching plan; the constraint condition at least includes the requirement of power grid frequency, voltage requirement and line loss requirement.

2. The power dispatching method of claim 1, wherein, The auxiliary decision information is obtained by the edge computing node according to the current running state and the historical running state, and the node power output and load running in a small time scale are predicted.

3. The power scheduling method of claim 1, wherein, The step of dispatching power grid data from the updated power grid database and allocating static security analysis calculation task to the edge computing node according to the computing resources and storage resources, comprising: sending analysis data to the edge computing node, and dispatching the power grid data from the updated power grid database and allocating the static security analysis calculation task to the edge computing node according to the computing resources and the storage resources; the analysis data includes calculation parameters and convergence criterion.

4. The power scheduling method of claim 1, wherein, The power system dispatching plan includes the output plan of power source body in a small time scale and the electricity consumption plan of load body in a small time scale.

5. The power scheduling method of claim 1, wherein, After the step of making the power system dispatching plan based on the calculation result under the constraint of the constraint condition, and satisfying the balance between power production and power consumption in a small time scale as the target, and outputting to the edge computing node, further comprising: receiving the dispatching request sent by the edge computing node and the running state of the power node after controlling the control equipment to execute the power system dispatching plan; In the case of receiving the dispatching request, the power system dispatching plan is adjusted based on the running state of the power node after controlling the control equipment to execute the power system dispatching plan; Send the adjusted power system dispatching plan to the edge computing node.

6. A power dispatching method characterized by, The application is applied to an edge computing node, which is configured at least in a power plant, an industrial load, a residential load and a new energy station, and comprises: Collecting a current operation state of a power node, predicting auxiliary decision information of the power node according to the current operation state and a historical operation state of the power node, and sending the current operation state and the auxiliary decision information to a cloud master station, so that the cloud master station updates a power grid database based on the current operation state and the auxiliary decision information, and allocates static security analysis calculation tasks to the edge computing node according to a computing resource and a storage resource and based on the updated power grid database and the power grid data; the operation state of the power node at least includes a connection relationship between the power node and a power grid, power supply parameters and models, a power load model, a transformer model, line impedance parameters and power flow data of the power node; Receiving power grid data and static security analysis calculation tasks sent by the cloud master station, and performing static security analysis calculation on the power grid data to obtain a calculation result and upload the calculation result to the cloud master station when the static security analysis calculation tasks are received; Receiving a power system dispatching plan sent by the cloud master station, and controlling a control device in the power node to execute the power system dispatching plan; the power system dispatching plan is formulated by the cloud master station under a constraint condition and with a goal of meeting a balance between power production and power consumption in a small time scale according to the calculation result; the constraint condition at least includes a power grid frequency requirement, a voltage requirement and a line loss requirement.

7. The power scheduling method of claim 6, wherein, The step of predicting the auxiliary decision information of the power node according to the current operation state and the historical operation state of the power node comprises: Predicting a node power output condition and a load operation condition in a small time scale according to the current operation state and the historical operation state of the power node to obtain the auxiliary decision information.

8. The power scheduling method of claim 6, wherein, After the step of receiving the power system dispatching plan sent by the cloud master station and controlling the control device in the power node to execute the power system dispatching plan, the method further comprises: Collecting an operation state of the power node after the control device executes the power system dispatching plan and sending the operation state to the cloud master station, and sending a dispatching request to the cloud master station; the dispatching request is used to instruct the cloud master station to adjust the power system dispatching plan based on the operation state of the power node after the control device executes the power system dispatching plan; Receiving an adjusted power system dispatching plan sent by the cloud master station, and controlling the control device in the power node to execute the adjusted power system dispatching plan.

9. A power dispatch system characterized by, The application comprises: a cloud master station and an edge computing node connected to the cloud master station; the cloud master station is used to execute the power dispatching method in any one of claims 1 to 5; the edge computing node is used to execute the power dispatching method in any one of claims 6 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the steps of the method in any one of claims 1 to 8. The computer program is executed by a processor to implement the steps of the method in any one of claims 1 to 8.

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