Ground-level power grid coordinated intelligent scheduling method and device based on edge calculation

Abstract

The invention relates to the related technical field of scheduling, in particular to a geographical-level power grid coordinated intelligent scheduling method and device based on edge computing. The method comprises the following steps: collecting distributed resource data of a geographic-level power grid; constructing an edge computing cluster; scheduling based on an edge computing cluster; wherein the step of constructing the edge computing cluster comprises the following substeps: determining a scheduling task and local-level power grid distributed resources; based on the scheduling task and the geographical-level power grid distributed resources, aggregating the geographical-level power grid distributed resources to obtain a plurality of geographical-level power grid distributed computing nodes; wherein each local-level power grid distributed computing node corresponds to a group of scheduling tasks; and the corresponding relation between the distributed computing nodes and the scheduling tasks of the geographic-level power grid is confirmed based on the principle of proximity.

Description

Technical Field

[0001] The present application relates to the technical field related to scheduling, and specifically to a method and device for coordinated intelligent scheduling of local power grids based on edge computing. Background Art

[0002] Under the background of energy structure transformation, the prefectural power grid is the key link and power distribution hub between large power grids and power users. The access of a large number of distributed power sources has promoted the evolution of the prefectural power grid, making the power flow distribution of the prefectural power grid change from fixed one-way to random two-way flow. Therefore, the dispatching mode of the prefectural power grid needs to be changed from passive switch regulation based on network structure to active optimization regulation based on power regulation in response to planned maintenance and unplanned failures. When the network is operating normally, various control methods should be actively used to optimize the operation mode, promote green power consumption, blockage relief, load balance, network loss reduction, voltage fluctuation reduction, peak-to-valley difference reduction, delay construction investment, etc., to comprehensively improve the economy, safety and cleanliness of the operation of the prefectural power grid.

[0003] Existing scheduling methods cannot meet the current scheduling needs. Summary of the invention

[0004] In view of this, the embodiments of the present application are committed to providing a method and device for coordinated intelligent scheduling of local power grids based on edge computing.

[0005] The first aspect of the present application provides a method for coordinated intelligent dispatching of a local power grid based on edge computing, comprising:

[0006] Collect distributed resource data of local power grid;

[0007] Build edge computing clusters;

[0008] Scheduling based on edge computing clusters;

[0009] Among them, building an edge computing cluster includes:

[0010] Determine dispatching tasks and distributed resources of local power grids;

[0011] Based on the scheduling task and the distributed resources of the prefecture-level power grid, the distributed resources of the prefecture-level power grid are aggregated to obtain a plurality of distributed computing nodes of the prefecture-level power grid;

[0012] Among them, each local-level power grid distributed computing node corresponds to a set of scheduling tasks.

[0013] In some embodiments, the scheduling based on the edge computing cluster includes:

[0014] The corresponding dispatching tasks are calculated based on the distributed computing nodes of each local power grid, and dispatching is carried out based on the calculation results.

[0015] In some embodiments, it also includes:

[0016] The scheduling tasks are distributed to the preset local-level power grid distributed computing nodes, so that each local-level power grid distributed computing node corresponds to a group of scheduling tasks.

[0017] In some embodiments, it also includes:

[0018] For each prefecture-level power grid distributed computing node, calculating whether the corresponding scheduling task exceeds the computing threshold of the prefecture-level power grid distributed computing node;

[0019] If so, the distributed resources of the prefecture-level power grid are reaggregated to obtain multiple prefecture-level power grid distributed computing nodes.

[0020] In some embodiments, the re-aggregating the distributed resources of the prefecture-level power grid to obtain a plurality of distributed computing nodes of the prefecture-level power grid further includes:

[0021] For each prefecture-level power grid distributed computing node, calculating whether the corresponding scheduling task exceeds the computing threshold of the prefecture-level power grid distributed computing node;

[0022] If so, adjust the distribution of the scheduling tasks to adjust the corresponding relationship between the distributed computing nodes of the local power grid and the scheduling tasks.

[0023] In some embodiments, the adjusting the distribution of the scheduling tasks includes:

[0024] Adjust part of the scheduling tasks corresponding to the target node to other local-level power grid distributed computing nodes;

[0025] Among them, the target node is the local-level power grid distributed computing node whose corresponding scheduling task exceeds its computing threshold.

[0026] In some embodiments, the scheduling based on the edge computing cluster further includes:

[0027] Construct a coordinated intelligent dispatching model for local power grids based on edge computing;

[0028] Based on the distributed computing nodes of each prefecture-level power grid, the prefecture-level power grid coordinated intelligent dispatching model is run to achieve dispatching;

[0029] The local power grid coordinated intelligent dispatching model includes: an upper optimization model and a lower optimization model;

[0030] The upper optimization model is a model in which the prefecture-level coordinated intelligent dispatching center optimizes the operation status and output power of centralized new energy stations, distributed renewable energy power stations, energy storage devices, and controllable loads according to the operation needs of the provincial power grid, with the distributed resource priority consumption and local power supply economy as the optimization goals, to achieve the purpose of coordinated optimization scheduling;

[0031] The objective function of the upper optimization model is:

[0032]

[0033] Where: F1 is the dispatching cost of the local power grid; is the total dispatching cost of centralized renewable energy power stations; is the output power of the centralized renewable energy power station at time t; For edge gateway j The total scheduling cost of the covered distributed resources; is the edge gateway e at time t j Output power of the distributed resources covered; is the total dispatch cost of power interaction between the prefecture-level power grid and the provincial main grid; is the interaction power between the prefecture-level power grid and the provincial main grid at time t;

[0034] The constraints of the upper optimization model include: power balance constraint and power output limit constraint, the formula is as follows:

[0035]

[0036] Where: is the total power load of the prefecture-level power grid at time t; It is the output limit of centralized new energy power station; They are edge gateways j The upper and lower limits of the output power of the covered distributed resources; They are the scope of power interaction for prefecture-level power grids that are mainly required at the provincial level;

[0037] The lower-layer optimization model is the optimal power dispatching scheme and control command calculated by each edge computing node according to the cloud service application layer, with the improvement of the power quality of the local power grid as the optimization goal, and according to the feeder operation requirements, the distributed resources within the coverage of each edge computing node are operated and optimized to obtain the model of the optimal operation control scheme of each distributed resource;

[0038] The objective function of the lower optimization model is:

[0039]

[0040] Where: f lossis the objective function of minimizing the active power loss of the line; f h The objective function for improving the harmonic problem of the local power grid; f u The objective function for improving the three-phase imbalance problem of the local power grid; F loss 、F h 、F u is the normalization coefficient of different objective functions; ω loss ,ω h ,ω u is the weight coefficient of the corresponding objective function, and the value corresponds to the priority of the objective item; N j,l For edge node gateway j The number of ground-level power grid lines in the covered area; H is the maximum harmonic number considered; is the active power loss of the φth phase of line k at the hth harmonic frequency, which is calculated based on the voltage difference between the two ends of the line; N j,n For edge node gateway j The number of grid nodes within the prefecture-level grid area covered; is the hth harmonic voltage of phase φ at node i; are the fundamental voltages of phases a, b, and c at node i respectively;

[0041] The constraints of the lower optimization model include network power flow constraints, node voltage amplitude constraints, and dispatchable distributed resource operation constraints;

[0042] Among them, the fundamental wave power flow constraint formula is as follows:

[0043]

[0044] Where: are the active and reactive powers of the dispatchable distributed resources connected between the φ phase and φ' phase of node i, respectively; are respectively the active and reactive power of the low voltage load between the φ phase and φ' phase connected to the node i; They are dispatchable distributed resources and fundamental current of low voltage area respectively; is the total injected fundamental current of the φ phase of node i; is the mutual admittance at the fundamental frequency between the φ phase of node i and the φ' phase of node j in the local grid;

[0045] The node voltage constraint formula is as follows:

[0046]

[0047] Where: U and They are the lower and upper limits of the fundamental voltage amplitude respectively;

[0048] The power quality constraint formula is as follows:

[0049]

[0050] Where: are the upper limits of node harmonic voltage and unbalanced voltage respectively

[0051] The formula for scheduling distributed resource operation constraints is as follows:

[0052]

[0053]

[0054] Where: It represents the rated capacity of the dispatchable distributed resource grid-connected inverter; It represents the rated current of the dispatchable distributed resource grid-connected inverter.

[0055] The present application also provides a local power grid coordinated intelligent dispatching device based on edge computing, including:

[0056] A collection module is used to collect distributed resource data of local power grids;

[0057] Building modules for building edge computing clusters;

[0058] Scheduling module, used for scheduling based on edge computing clusters;

[0059] Among them, building an edge computing cluster includes:

[0060] Determine dispatching tasks and distributed resources of local power grids;

[0061] Based on the scheduling task and the distributed resources of the prefecture-level power grid, the distributed resources of the prefecture-level power grid are aggregated to obtain a plurality of distributed computing nodes of the prefecture-level power grid;

[0062] Among them, each local-level power grid distributed computing node corresponds to a set of scheduling tasks.

[0063] The present application also provides an electronic device, including:

[0064] A processor, and a memory for storing a program executable by the processor;

[0065] The processor is used to implement the above-mentioned edge computing-based coordinated intelligent scheduling method for local power grids by running the program in the memory.

[0066] The present application also provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor enables the processor to execute the above-mentioned edge computing-based coordinated intelligent scheduling method for local power grids.

[0067] The present application provides a method for coordinated intelligent dispatching of prefecture-level power grids based on edge computing, which collects distributed resource data of prefecture-level power grids; constructs edge computing clusters; and performs dispatching based on edge computing clusters; wherein, constructing edge computing clusters includes: determining dispatching tasks and prefecture-level power grid distributed resources; based on the dispatching tasks and prefecture-level power grid distributed resources, aggregating prefecture-level power grid distributed resources to obtain multiple prefecture-level power grid distributed computing nodes; wherein, each prefecture-level power grid distributed computing node corresponds to a group of dispatching tasks; and the correspondence between prefecture-level power grid distributed computing nodes and dispatching tasks is confirmed based on the principle of proximity. In this way, constructing edge computing clusters allows the system to dynamically adjust resource allocation and computing tasks according to actual needs, thereby adapting to the expansion of power grid scale and changes in operating conditions. By aggregating prefecture-level power grid distributed resources, multiple distributed computing nodes are formed, each of which corresponds to a group of dispatching tasks, so that computing resources and dispatching tasks can be allocated more efficiently, reducing resource waste. The edge computing cluster model allows data to be processed near the location where the data is generated, reducing the time for data transmission to the central server, and improving the response speed and real-time processing capability of the system. The distributed characteristics of the edge computing cluster mean that even if a node fails, other nodes can still continue to work, thereby improving the stability and reliability of the entire power grid dispatching system. By dynamically reconstructing the cluster architecture, the system can intelligently distribute the computing load of each edge node to achieve load balancing and avoid overloading of certain nodes and affecting overall performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0069] Figure 1 It is a flow chart of a method for coordinated intelligent scheduling of local power grids based on edge computing provided in one embodiment of the present application.

[0070] Figure 2 This is a schematic diagram of an edge node provided by an embodiment of the present application.

[0071] Figure 3It is a partial flow chart of a method for coordinated intelligent scheduling of local power grids based on edge computing provided in another embodiment of the present application.

[0072] Figure 4 It is a structural diagram of a local power grid coordinated intelligent scheduling device based on edge computing provided by an embodiment of the present application.

[0073] Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0074] 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.

[0075] Application Overview

[0076] Under the background of energy structure transformation, the prefectural power grid is the key link and power distribution hub between large power grids and power users. The access of a large number of distributed power sources has promoted the evolution of the prefectural power grid, making the power flow distribution of the prefectural power grid change from fixed one-way to random two-way flow. Therefore, the dispatching mode of the prefectural power grid needs to be changed from passive switch regulation based on network structure to active optimization regulation based on power regulation in response to planned maintenance and unplanned failures. When the network is operating normally, various control methods should be actively used to optimize the operation mode, promote green power consumption, blockage relief, load balance, network loss reduction, voltage fluctuation reduction, peak-to-valley difference reduction, delay construction investment, etc., to comprehensively improve the economy, safety and cleanliness of the operation of the prefectural power grid.

[0077] In order to better adapt to the access of a large number of distributed power sources to the prefectural power grid and improve the flexible control capability of the power grid in areas with a high proportion of new energy, it is necessary to collect, process and analyze the massive data generated by the system in a timely manner so as to be used for the optimized operation and scheduling of the power system. As an emerging computing mode, edge computing technology has the characteristics of low latency, high bandwidth and low energy consumption compared with traditional cloud computing. It can improve the data processing efficiency and real-time performance of the smart grid and ensure the stable and safe operation of the smart grid system. The traditional centralized computing mode is prone to single point failure of the system, while edge computing technology distributes computing and data processing functions on different edge nodes to achieve parallel processing and distributed storage of data. When a certain edge node of the system fails, other nodes can still work normally, thereby improving the reliability and robustness of the system to ensure the stable operation of the smart grid. In order to solve the shortcomings of the existing technology and solutions, the present invention proposes a method and system for coordinated intelligent scheduling of prefectural power grids based on edge computing. Through the edge computing cluster, a stable, reliable, safe, intelligent, dynamically and autonomously reconfigurable multi-group, clustered prefectural power grid coordinated intelligent scheduling system is constructed; the system can realize the coordinated interaction of distributed resources according to the feeder operation requirements and the provincial main network operation requirements, and improve the flexible control capability of the prefectural power grid.

[0078] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0079] Exemplary Methods

[0080] Figure 1 1 is a flow chart of a method for coordinated intelligent dispatching of local power grids based on edge computing provided by an embodiment of the present application. Figure 1 As shown, the method includes the following contents.

[0081] Step S110, collecting distributed resource data of the local power grid;

[0082] Step S120, building an edge computing cluster;

[0083] Step S130, scheduling based on the edge computing cluster;

[0084] Among them, building an edge computing cluster includes: determining scheduling tasks and distributed resources of the prefecture-level power grid; based on the scheduling tasks and distributed resources of the prefecture-level power grid, aggregating the distributed resources of the prefecture-level power grid to obtain multiple distributed computing nodes of the prefecture-level power grid; wherein each distributed computing node of the prefecture-level power grid corresponds to a group of scheduling tasks; the correspondence between the distributed computing nodes of the prefecture-level power grid and the scheduling tasks is confirmed based on the principle of proximity.

[0085] Specifically, in step S110, collecting distributed resource data of the prefecture-level power grid refers to accessing, monitoring and analyzing distributed resource data of the prefecture-level power grid.

[0086] By using sensors and controllers distributed at different nodes of the power grid, the operating data of distributed new energy equipment, energy storage equipment, and controllable loads are monitored, collected, and analyzed. Based on the distribution transformer monitoring terminal equipment, key information such as the amplitude and phase of the voltage and current on the low-voltage side of each distribution transformer in the local power grid is collected in real time, making distributed resources "observable, measurable, and controllable."

[0087] Step S120: Building an edge computing cluster, as follows:

[0088] The edge computing cluster model can provide a reliable computing service foundation for the coordinated intelligent dispatching business of the prefecture-level power grid, reducing the time and resources consumed by processing tasks. To design the coordinated intelligent dispatching method of the prefecture-level power grid, it is necessary to first establish an edge computing cluster model based on the edge computing principle, autonomously divide and aggregate the distributed resources of the prefecture-level power grid through the model, and centrally manage and control it based on the prefecture-level power grid cloud service hub node, and dynamically reconstruct the cluster architecture according to the data scale and computing resource requirements of the edge node scheduling task. Then, the edge gateway in the prefecture-level power grid provides data support and scheduling computing services to the dispatchable distributed resources and cloud service center based on the edge computing modules, data storage and communication modules, data routing and other devices deployed in the system, and can also transmit the calculated data and collected data to the adjacent edge computing layer gateway, distribute the computing tasks that the gateway needs to complete, realize the collaborative computing of multiple edge computing gateways, and more efficiently complete the scheduling optimization of edge nodes in the prefecture-level power grid. When the amount of computing tasks that the edge cluster layer gateway is responsible for is small, the current gateway can be used to complete the scheduling task calculation.

[0089] In step S130, scheduling is performed based on the edge computing cluster; specifically, the method of coordinated intelligent scheduling of local power grids based on edge computing is studied.

[0090] In the process of overall optimization and dispatching of prefecture-level power grids, it is necessary to give full play to the ability of multi-node cloud-edge collaborative intelligent dispatching, and actively control distributed resources to achieve collaborative interaction according to the feeder operation requirements and the provincial main grid operation requirements. Therefore, a method for coordinated intelligent dispatching of prefecture-level power grids based on edge computing is studied and established, and it is constructed as a two-layer optimization dispatching model. Among them, the upper optimization model is the provincial main grid dispatching plan data received by the prefecture-level power grid coordinated intelligent dispatching center, the centralized new energy sites in the prefecture-level power grid, and the distributed wind and solar power stations, energy storage stations, controllable loads and other equipment operation data uploaded by the edge gateway. With the minimum operation and dispatching cost of the prefecture-level power grid as the optimization goal, the intelligent optimization algorithm is used to solve the problem, and the dispatching optimization of the centralized new energy sites in the prefecture-level power grid and the areas covered by different edge computing gateways is completed, and the optimal power supply dispatching plan of the prefecture-level power grid is obtained; the lower optimization model is the edge computing gateway based on the optimal power supply dispatching plan calculated by the cloud service center, and the distributed resources within the coverage of each edge computing gateway are operated and optimized to obtain the optimal operation control plan for each distributed resource.

[0091] Furthermore, the construction of edge computing clusters specifically includes the following contents: The edge computing platform supporting edge services is composed of a cloud service center and several geographically distributed edge nodes, which together form an internally interactively manageable edge computing service cluster. Different edge nodes in the cluster are connected to the cloud service center through the cloud-edge network to achieve cloud-edge collaboration. In order to meet the high efficiency and low latency requirements of power grid dispatching, an adaptive dynamic reconstruction mechanism is proposed, which enables the edge computing cluster to be dynamically reconstructed according to the actual operation requirements of the power grid. Based on the intelligent load balancing algorithm, the computing load of each edge node in the cluster is relatively balanced to ensure the operation efficiency of the entire edge computing cluster and avoid overloading of some nodes and affecting the overall performance.

[0092] First, the edge computing cluster is modeled and analyzed. Figure 2 Assume that there are J edge nodes in the system, and E represents the set of all nodes, that is, E = {e1, e2, …, e J For each edge gateway of node e, there is a certain upper limit on the allocation of computing resources α, storage resources β, and network bandwidth resources γ, which is represented by a vector containing its available capacity: j =(α j ,β j ,γ j ), Let A = {a1, a2, ..., a N} is the set of scheduling plans distributed to edge nodes. Each edge scheduling plan can be modeled as a directed acyclic graph G=<MS,LK> The graph model consists of a series of distributed resources that cooperate with each other and their dependencies. MS represents the distributed resource set, that is, MS = {ms1, ms2, ..., ms K}, LK represents the dependency set between distributed resources. The distributed resource msk can be represented by a resource vector: msk = (Ck, Mk, Bk), where C k ,M k ,B k Represents the scheduling of distributed resources ms k Computational requirements, memory requirements, and bandwidth resource requirements.

[0093] Define a binary variable x k,i,j To represent task a i Whether it is dispatched to the edge gateway j , when task a i Dispatched to the edge gateway j When x k,i,j =1, otherwise x k,i,j = 0. Then the computing task set of all edge gateways of the prefecture-level power grid is as follows:

[0094] X={xk,i,j}(1)

[0095] Where: X is the set of distributed resource scheduling tasks of the prefecture-level power grid; k,i,j is the corresponding value in the set, x k,i,j =1 means distributed resource ms k The scheduling computing task a i Assigned to edge gateway j .

[0096] According to the principle that all computing tasks must be completed by at least one edge computing gateway, the following restrictions must be met:

[0097]

[0098] Where: the first item is to ensure that the computing task is completed by the edge gateway; the second, third, and fourth items are to ensure that the edge gateway e j The constraints of computing power and data storage capacity response situation; For edge gateway j The response amount of computing power, data storage capacity, and network bandwidth transmission capacity, α j , β j , γ j is the corresponding upper limit; C k,iCalculate demand for scheduling tasks; M k,i To meet the data storage requirements for scheduling tasks; B k,i is the bandwidth transmission requirement for scheduling tasks; A is the set of optimized scheduling computing tasks in the system.

[0099] Therefore, the total optimization scheduling computing task demand load in the system can be expressed as:

[0100]

[0101] The cloud service center optimizes the scheduling of the prefectural power grid and the provincial main grid, as well as the scheduling of power grids in different edge gateway coverage areas based on the received scheduling optimization data and energy equipment operation data, and conducts unified coordinated scheduling optimization calculations for the entire prefectural power grid. The calculation results are also sent to the edge computing layer through the data network communication layer; the status data perception hardware collects the operating status data and ledger data of the terminal device entity, and stores them in the edge computing node database respectively; then, the optimization scheduling tasks are started at each energy equipment terminal and the low-voltage side of the distribution, and the computing tasks are allocated, the corresponding optimization calculations are performed, and the operation scheduling commands are sent to the terminal device entity according to the calculation results to adjust the equipment operation status. At the same time, all the calculation results are summarized and merged and sent to the cloud service center.

[0102] When the computing load distribution of edge nodes in the edge computing cluster is uneven, the dynamic balance of the total computing task demand load of the system can be achieved through dynamic reconstruction of the dependency set LK between distributed resources and interactive management within the edge computing service cluster based on formula (7). The dynamic reconstruction process is as follows: Figure 3 shown.

[0103]

[0104] Specifically, the scheduling tasks are distributed to preset local-level power grid distributed computing nodes, so that each local-level power grid distributed computing node corresponds to a group of scheduling tasks.

[0105] For each prefecture-level power grid distributed computing node, calculating whether the corresponding scheduling task exceeds the computing threshold of the prefecture-level power grid distributed computing node;

[0106] If so, the distributed resources of the prefecture-level power grid are reaggregated to obtain multiple prefecture-level power grid distributed computing nodes.

[0107] The re-aggregation of the prefecture-level power grid distributed resources to obtain a plurality of prefecture-level power grid distributed computing nodes further includes:

[0108] For each prefecture-level power grid distributed computing node, calculating whether the corresponding scheduling task exceeds the computing threshold of the prefecture-level power grid distributed computing node;

[0109] If so, adjust the distribution of the scheduling tasks to adjust the corresponding relationship between the distributed computing nodes of the local power grid and the scheduling tasks.

[0110] Part of the scheduling tasks corresponding to the target node is adjusted to other local-level power grid distributed computing nodes; wherein the target node is a local-level power grid distributed computing node whose corresponding scheduling task exceeds its computing threshold.

[0111] Such a setting can achieve dynamic balancing of the system's total computing task demand load.

[0112] Furthermore, the scheduling based on the edge computing cluster includes:

[0113] The corresponding dispatching tasks are calculated based on the distributed computing nodes of each local power grid, and dispatching is carried out based on the calculation results.

[0114] Specifically, the scheduling based on the edge computing cluster includes:

[0115] Construct a coordinated intelligent dispatching model for local power grids based on edge computing;

[0116] Based on the distributed computing nodes of each prefecture-level power grid, the prefecture-level power grid coordinated intelligent dispatching model is run to achieve dispatching;

[0117] The local power grid coordinated intelligent dispatching model includes: an upper optimization model and a lower optimization model;

[0118] The upper optimization model is a model in which the prefecture-level coordinated intelligent dispatching center optimizes the operation status and output power of centralized new energy stations, distributed renewable energy power stations, energy storage devices, and controllable loads according to the operation needs of the provincial power grid, with the distributed resource priority consumption and local power supply economy as the optimization goals, to achieve the purpose of coordinated optimization scheduling;

[0119] The objective function of the upper optimization model is:

[0120]

[0121] Where: F1 is the dispatching cost of the local power grid; is the total dispatching cost of centralized renewable energy power stations; is the output power of the centralized renewable energy power station at time t; For edge gateway j The total scheduling cost of the covered distributed resources; is the edge gateway e at time t j Output power of the distributed resources covered; is the total dispatch cost of power interaction between the prefecture-level power grid and the provincial main grid; is the interaction power between the prefecture-level power grid and the provincial main grid at time t;

[0122] The constraints of the upper optimization model include: power balance constraint and power output limit constraint, the formula is as follows:

[0123]

[0124] Where: is the total power load of the prefecture-level power grid at time t; It is the output limit of centralized new energy power station; They are edge gateways j The upper and lower limits of the output power of the covered distributed resources; They are the scope of power interaction for prefecture-level power grids that are mainly required at the provincial level;

[0125] The lower-layer optimization model is the optimal power dispatching scheme and control command calculated by each edge computing node according to the cloud service application layer, with the improvement of the power quality of the local power grid as the optimization goal, and according to the feeder operation requirements, the distributed resources within the coverage of each edge computing node are operated and optimized to obtain the model of the optimal operation control scheme of each distributed resource;

[0126] The objective function of the lower optimization model is:

[0127]

[0128] Where: f loss is the objective function of minimizing the active power loss of the line; f h The objective function for improving the harmonic problem of the local power grid; f u The objective function for improving the three-phase imbalance problem of the local power grid; F loss 、F h 、F u is the normalization coefficient of different objective functions; ω loss ,ω h ,ω u is the weight coefficient of the corresponding objective function, and the value corresponds to the priority of the objective item; N j,l For edge node gateway j The number of ground-level power grid lines in the covered area; H is the maximum harmonic number considered; is the active power loss of the φth phase of line k at the hth harmonic frequency, which is calculated based on the voltage difference between the two ends of the line; N j,n For edge node gateway j The number of grid nodes within the prefecture-level grid area covered; is the hth harmonic voltage of phase φ at node i; are the fundamental voltages of phases a, b, and c at node i respectively;

[0129] The constraints of the lower optimization model include network power flow constraints, node voltage amplitude constraints, and dispatchable distributed resource operation constraints;

[0130] Among them, the fundamental wave power flow constraint formula is as follows:

[0131]

[0132]

[0133] Where: are the active and reactive powers of the dispatchable distributed resources connected between the φ phase and φ' phase of node i, respectively; are respectively the active and reactive power of the low voltage load between the φ phase and φ' phase connected to the node i; They are dispatchable distributed resources and fundamental current of low voltage area respectively; is the total injected fundamental current of the φ phase of node i; is the mutual admittance at the fundamental frequency between the φ phase of node i and the φ' phase of node j in the local grid;

[0134] The node voltage constraint formula is as follows:

[0135]

[0136] Where: U and They are the lower and upper limits of the fundamental voltage amplitude respectively;

[0137] The power quality constraint formula is as follows:

[0138]

[0139] Where: are the upper limits of node harmonic voltage and unbalanced voltage respectively

[0140] The formula for scheduling distributed resource operation constraints is as follows:

[0141]

[0142] Where: It represents the rated capacity of the dispatchable distributed resource grid-connected inverter; It represents the rated current of the dispatchable distributed resource grid-connected inverter.

[0143] Such a setting can realize scheduling calculations based on a pre-built dynamically and autonomously reconfigurable edge computing cluster.

[0144] Exemplary Devices

[0145] The device embodiments of the present application can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0146] Figure 4 FIG. 1 is a block diagram of a local power grid coordinated intelligent dispatching device based on edge computing provided by an embodiment of the present application. Figure 4 As shown, the device comprises:

[0147] A collection module 41 is used to collect distributed resource data of local power grids;

[0148] A construction module 42 is used to construct a local power grid edge computing cluster model;

[0149] A scheduling module 43, used for scheduling based on a local power grid edge computing cluster model;

[0150] Among them, the construction of the local power grid edge computing cluster model includes:

[0151] Determine dispatching tasks and distributed resources of local power grids;

[0152] Based on the scheduling task and the distributed resources of the prefecture-level power grid, the distributed resources of the prefecture-level power grid are aggregated to obtain a plurality of distributed computing nodes of the prefecture-level power grid;

[0153] Among them, each local-level power grid distributed computing node corresponds to a set of scheduling tasks.

[0154] This embodiment also provides a local power grid coordinated intelligent dispatching system based on edge computing, the system includes terminal device entities, state data perception hardware, edge computing gateways, data network communication equipment, cloud service centers and other hardware and software parts;

[0155] The terminal device entity includes various distributed resource devices and their operation control modules. Its main task is to control and adjust the operation status of distributed resources through the device operation control module.

[0156] The status data perception hardware is composed of various data acquisition devices and measurement modules. Its main task is to assist the edge computing gateway and the coordinated intelligent dispatching center to perceive the operating status of various types of distributed resources connected to the local power grid.

[0157] The edge computing gateway consists of an edge computing module and a data storage device. Its main task is to calculate the optimal operation plan for each distributed resource in the responsible area, including equipment operating status, output level, etc.

[0158] Data network communication equipment includes data routing, wireless modules and other components. Its main task is to assist the communication and transmission of status data and dispatching data control commands between multiple layers such as status data perception hardware, edge computing gateways, and coordinated intelligent dispatching centers, ensuring that data can be transmitted quickly and losslessly.

[0159] As the management center of power generation, distribution and power supply of prefectural-level power grids, the coordinated intelligent dispatching center mainly undertakes the tasks of providing data storage, reading, calculation, analysis and dynamic display services for the centralized control of prefectural-level power grids. Through calculation and analysis, it calculates the optimal energy supply plan between power grids in different regions and realizes the optimization of network energy between regions in the new energy power grid.

[0160] The coordinated intelligent dispatching system includes a load and power generation forecasting module, a day-ahead intelligent dispatching module, an intraday forward-looking dispatching module, and a distributed resource monitoring module.

[0161] The load and power generation forecast module generates short-term and ultra-short-term load forecast data and new energy power station power output forecast data for local power grids based on historical data, meteorological forecast information and other data. The forecast results of this module are used to output to the day-ahead intelligent scheduling and intraday forward scheduling modules.

[0162] The day-ahead intelligent dispatching module generates the day-ahead dispatching plan through intelligent algorithms based on load forecasting and new energy generation forecasting. This module comprehensively considers multiple factors such as power supply of prefecture-level power grids, feeder operation requirements, and provincial main grid operation requirements, and formulates a day-ahead dispatching plan for prefecture-level power grids to ensure the economic and reliable power supply of the power grid in the future period.

[0163] The intraday forward-looking dispatch module is responsible for monitoring and analyzing the current operating status, combining the real-time data of load and power generation to adjust the operation strategy of the power system in real time. This module has flexible real-time dispatching capabilities and can respond quickly to the power system according to actual conditions to ensure the smooth operation of the system during the day.

[0164] The distributed resource monitoring module is used to monitor and control the operating status of each distributed resource device in real time. Through this module, the system can obtain the real-time operating data of distributed new energy equipment, energy storage equipment and controllable loads, and realize the precise scheduling and management of these devices. The module is also responsible for assisting the edge computing gateway to perceive and collect the status data of each distributed resource, and provide real-time support for the overall coordinated scheduling of the system.

[0165] The above modules together constitute the core of the coordinated intelligent dispatching system. Through collaborative work, they can achieve efficient dispatching of local power grids and optimal utilization of new energy resources, thereby improving the overall operating efficiency of the power system.

[0166] Exemplary Electronic Devices

[0167] Below, reference Figure 5 To describe an electronic device according to an embodiment of the present application. Figure 5 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0168] like Figure 5 As shown, electronic device 500 includes one or more processors 510 and memory 520 .

[0169] The processor 510 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 500 to perform desired functions.

[0170] The memory 520 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 510 may run the program instructions to implement the above-described embodiments of the present application, the coordinated intelligent dispatching method for local power grids based on edge computing and / or other desired functions. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0171] In one example, the electronic device 500 may further include: an input device 530 and an output device 540 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0172] In addition, the input device 530 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 540 may output various information to the outside, including analysis results, etc. The output device 540 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0173] Of course, to simplify, Figure 5 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0174] Exemplary computer program products and computer-readable storage media

[0175] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the local power grid coordinated intelligent scheduling method based on edge computing according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0176] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0177] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the method for coordinated intelligent scheduling of local power grids based on edge computing according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0178] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0179] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for coordinated intelligent dispatching of local power grids based on edge computing, characterized in that: include: Collect distributed resource data of local power grid; Build edge computing clusters; Scheduling based on edge computing clusters; Among them, building an edge computing cluster includes: Determine dispatching tasks and distributed resources of local power grids; Based on the scheduling task and the distributed resources of the prefecture-level power grid, the distributed resources of the prefecture-level power grid are aggregated to obtain a plurality of distributed computing nodes of the prefecture-level power grid; Among them, each prefecture-level power grid distributed computing node corresponds to a group of scheduling tasks; the correspondence between the prefecture-level power grid distributed computing nodes and the scheduling tasks is confirmed based on the proximity principle.

2. The method for coordinated intelligent dispatching of local power grids based on edge computing according to claim 1 is characterized in that: The scheduling based on the edge computing cluster includes: The corresponding dispatching tasks are calculated based on the distributed computing nodes of each local power grid, and dispatching is carried out based on the calculation results.

3. According to claim 1, the method for coordinated intelligent dispatching of local power grids based on edge computing is characterized in that: Also includes: The scheduling tasks are distributed to the preset local-level power grid distributed computing nodes, so that each local-level power grid distributed computing node corresponds to a group of scheduling tasks.

4. According to claim 3, the method for coordinated intelligent dispatching of local power grids based on edge computing is characterized in that: Also includes: For each prefecture-level power grid distributed computing node, calculating whether the corresponding scheduling task exceeds the computing threshold of the prefecture-level power grid distributed computing node; If so, the distributed resources of the prefecture-level power grid are reaggregated to obtain multiple prefecture-level power grid distributed computing nodes.

5. According to claim 4, the method for coordinated intelligent dispatching of local power grids based on edge computing is characterized in that: The re-aggregation of the prefecture-level power grid distributed resources to obtain a plurality of prefecture-level power grid distributed computing nodes further includes: For each prefecture-level power grid distributed computing node, calculating whether the corresponding scheduling task exceeds the computing threshold of the prefecture-level power grid distributed computing node; If so, adjust the distribution of the scheduling tasks to adjust the corresponding relationship between the distributed computing nodes of the local power grid and the scheduling tasks.

6. According to claim 5, the method for coordinated intelligent dispatching of local power grids based on edge computing is characterized in that: The distribution of the adjustment scheduling task includes: Adjust part of the scheduling tasks corresponding to the target node to other local-level power grid distributed computing nodes; Among them, the target node is the local-level power grid distributed computing node whose corresponding scheduling task exceeds its computing threshold.

7. The method for coordinated intelligent dispatching of local power grids based on edge computing according to claim 6 is characterized in that: The scheduling based on the edge computing cluster also includes: Construct a coordinated intelligent dispatching model for local power grids based on edge computing; Based on the distributed computing nodes of each prefecture-level power grid, the prefecture-level power grid coordinated intelligent dispatching model is run to achieve dispatching; The local power grid coordinated intelligent dispatching model includes: an upper optimization model and a lower optimization model; The upper optimization model is a model in which the prefecture-level coordinated intelligent dispatching center optimizes the operation status and output power of centralized new energy stations, distributed renewable energy power stations, energy storage devices, and controllable loads according to the operation needs of the provincial power grid, with the distributed resource priority consumption and local power supply economy as the optimization goals, to achieve the purpose of coordinated optimization scheduling; The objective function of the upper optimization model is: Where: F1 is the dispatching cost of the local power grid; is the total dispatching cost of centralized renewable energy power stations; is the output power of the centralized renewable energy power station at time t; For edge gateway j The total scheduling cost of the covered distributed resources; is the edge gateway e at time t j Output power of the distributed resources covered; is the total dispatch cost of power interaction between the prefecture-level power grid and the provincial main grid; is the interaction power between the prefecture-level power grid and the provincial main grid at time t; The constraints of the upper optimization model include: power balance constraint and power output limit constraint, the formula is as follows: Where: is the total power load of the prefecture-level power grid at time t; It is the output limit of centralized new energy power station; They are edge gateways j The upper and lower limits of the output power of the covered distributed resources; They are the scope of power interaction for prefecture-level power grids that are mainly required at the provincial level; The lower-layer optimization model is the optimal power dispatching scheme and control command calculated by each edge computing node according to the cloud service application layer, with the improvement of the power quality of the local power grid as the optimization goal, and according to the feeder operation requirements, the distributed resources within the coverage of each edge computing node are operated and optimized to obtain the model of the optimal operation control scheme of each distributed resource; The objective function of the lower optimization model is: Where: f loss is the objective function of minimizing the active power loss of the line; f h The objective function for improving the harmonic problem of the local power grid; f u The objective function for improving the three-phase imbalance problem of the local power grid; F loss 、F h 、F u is the normalization coefficient of different objective functions; ω loss ,ω h ,ω u is the weight coefficient of the corresponding objective function, and the value corresponds to the priority of the objective item; N j,l For edge node gateway j The number of ground-level power grid lines in the covered area; H is the maximum harmonic number considered; is the active power loss of the φth phase of line k at the hth harmonic frequency, calculated based on the voltage difference between the two ends of the line; N j,n For edge node gateway j The number of grid nodes within the prefecture-level grid area covered; is the hth harmonic voltage of phase φ at node i; are the fundamental voltages of phases a, b, and c at node i respectively; The constraints of the lower optimization model include network power flow constraints, node voltage amplitude constraints, and dispatchable distributed resource operation constraints; Among them, the fundamental wave power flow constraint formula is as follows: Where: are the active and reactive powers of the dispatchable distributed resources connected between the φ phase and φ' phase of node i, respectively; are respectively the active and reactive power of the low voltage load between the φ phase and φ' phase connected to the node i; They are dispatchable distributed resources and fundamental current of low voltage area respectively; is the total injected fundamental current of the φ phase of node i; is the mutual admittance at the fundamental frequency between the φ phase of node i and the φ' phase of node j in the local grid; The node voltage constraint formula is as follows: Where: U and They are the lower and upper limits of the fundamental voltage amplitude respectively; The power quality constraint formula is as follows: Where: are the upper limits of node harmonic voltage and unbalanced voltage respectively The formula for scheduling distributed resource operation constraints is as follows: Where: It represents the rated capacity of the dispatchable distributed resource grid-connected inverter; It represents the rated current of the dispatchable distributed resource grid-connected inverter.

8. A coordinated intelligent dispatching device for local power grid based on edge computing, characterized in that: include: A collection module is used to collect distributed resource data of local power grids; Building modules for building edge computing clusters; Scheduling module, used for scheduling based on edge computing clusters; Among them, building an edge computing cluster includes: Determine dispatching tasks and distributed resources of local power grids; Based on the scheduling task and the distributed resources of the prefecture-level power grid, the distributed resources of the prefecture-level power grid are aggregated to obtain a plurality of distributed computing nodes of the prefecture-level power grid; Among them, each local-level power grid distributed computing node corresponds to a set of scheduling tasks.

9. An electronic device, characterized in that: include: A processor, and a memory for storing a program executable by the processor; The processor is used to implement the local power grid coordinated intelligent scheduling method based on edge computing as described in any one of claims 1 to 7 by running the program in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the method for coordinated intelligent scheduling of local power grids based on edge computing as described in any one of claims 1 to 7.

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