Power grid distributed new energy consumption scheduling optimization method based on cloud edge fusion calculation

Abstract

The invention discloses a power grid distributed new energy consumption scheduling optimization method based on cloud edge fusion calculation, and belongs to the technical field of power grid scheduling control. The power grid distributed new energy consumption scheduling optimization method based on cloud edge fusion calculation comprises the steps of performing edge processing on power generation and adjustable load operation state data obtained in real time through edge equipment; transmitting the data subjected to edge processing to a cloud for operation condition design and constraint fusion calculation to obtain corresponding distributed new energy operation demand load constraints and adjustable capacity load constraints under different operation conditions; according to the method, edge computing nodes are introduced by configuring edge equipment at each power grid sub-node to perform edge data processing on distributed new energy power generation data and adjustable load operation state data, so that corresponding distributed new energy power generation power, adjustable load requirements and distributed energy storage equipment state data are obtained.

Description

Technical Field

[0001] The present invention relates to the field of power grid dispatching and control technology, and in particular to a method for optimizing the dispatching of distributed renewable energy consumption in a power grid based on cloud-edge fusion computing. Background Art

[0002] With the rapid increase in the access capacity of distributed renewable energy, the impact of distributed renewable energy with both volatility and randomness on the operating status of the power system is increasing, resulting in increasing dispatching and regulation pressure faced by dispatching and control personnel at all levels of the power system during operation, and there is an urgent need to explore and develop corresponding technical solutions.

[0003] To this end, it is planned to build a coordinated operation and regulation strategy for the main distribution network based on the cloud-edge fusion technology framework, on the basis of obtaining the perception and control capabilities of the underlying distributed resources and adjustable loads, to improve the safe operation level of the power system and the absorption capacity of the distributed resource clusters with low voltage access, aiming to cope with the stable operation and control challenges of high-proportion new energy and high-proportion power electronic equipment access to the power grid. In view of the insufficient quantitative assessment of the regulation capability, coordination capability and operation demand risk of massive distributed generation, load, energy storage and other controllable units after the low-voltage access of the distribution network to distributed resources, the cloud-edge fusion distribution network with multiple types of distributed resource clusters is proposed. Active regulation capability and operation demand assessment and analysis methods and demonstration applications are proposed, which can improve the overall goal of safety and economy of power grid operation. However, the traditional dispatching and control methods lack the perception and regulation control capabilities of low-voltage access distributed new energy and adjustable system loads, which leads to greater dispatching pressure on power system dispatchers during operation. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is that the traditional dispatching and control methods lack the ability to perceive and adjust the distributed new energy sources with low voltage access and the system loads with adjustable capabilities, which leads to the problem that the power system dispatchers face greater dispatching pressure during operation.

[0006] To achieve the above purpose, the present invention provides the following technical solution: a method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing, comprising the steps of:

[0007] The real-time data of power generation and adjustable load operation status is obtained through edge devices for edge processing;

[0008] The edge processed data is transmitted to the cloud for operation condition design and constraint fusion calculation to obtain the corresponding distributed new energy operation demand load constraints and adjustable capacity load constraints under different operation conditions;

[0009] Based on load constraints and adjustable capacity load constraints, risk assessment and analysis are performed on the corresponding grid sub-nodes in the main distribution network of the power system to obtain the corresponding distributed renewable energy risk level of each grid sub-node under different operating conditions;

[0010] According to the risk level of new energy, the coordinated dispatching of each grid sub-node in the main distribution network of the power system is optimized, and an optimization strategy for the coordinated dispatching of the main distribution network of the power system is generated to execute the coordinated dispatching of distributed new energy consumption between each grid sub-node of the corresponding main distribution network.

[0011] As a further solution of the present invention: the step of edge processing the real-time power generation and adjustable load operation status data by edge devices includes:

[0012] Through distributed sensors and monitoring equipment, real-time acquisition of distributed renewable energy power generation data and adjustable load operation status data corresponding to each grid sub-node in the main distribution network of the power system;

[0013] By designing the edge device configuration for each grid sub-node in the main distribution network of the power system, a design blueprint for the edge device configuration corresponding to each grid sub-node is generated;

[0014] Based on the edge device configuration design blueprint corresponding to each grid sub-node, the corresponding grid sub-nodes in the main distribution network of the power system are integrated with the edge computing node functions to obtain the corresponding edge computing nodes in the main distribution network of the power system;

[0015] The corresponding edge computing nodes in the main distribution network of the power system are used to perform edge data processing on the distributed renewable energy power generation data and the adjustable load operation status data, and the distributed renewable energy power generation power, adjustable load demand and distributed energy storage equipment status data corresponding to each edge computing node are obtained.

[0016] As a further solution of the present invention: based on the edge device configuration design blueprint corresponding to each grid sub-node, the corresponding grid sub-node in the main distribution network of the power system is integrated with the edge computing node function to obtain the corresponding edge computing nodes in the main distribution network of the power system, including:

[0017] Use the corresponding edge computing nodes in the main distribution network of the power system to monitor the power generation operation time of distributed renewable energy power generation data, and obtain the distributed renewable energy power generation operation time corresponding to each edge computing node;

[0018] Based on the distributed renewable energy power generation operation time corresponding to each edge computing node, the distributed renewable energy power generation data corresponding to the power generation amount and environmental conditions in the operation time are calculated to obtain the distributed renewable energy power generation power corresponding to each edge computing node;

[0019] Use the corresponding edge computing nodes in the main distribution network of the power system to perform edge load operation response analysis on the adjustable load operation status data to obtain the adjustable load operation response data corresponding to each edge computing node;

[0020] According to the adjustable load operation response data corresponding to each edge computing node, the load operation demand inference analysis is performed on the corresponding edge computing nodes in the main distribution network of the power system to obtain the adjustable load demand corresponding to each edge computing node;

[0021] Based on the distributed renewable energy power generation power and adjustable load demand corresponding to each edge computing node, the energy storage status of the energy storage devices in the grid sub-nodes corresponding to each edge computing node is evaluated and analyzed to obtain the status data of the distributed energy storage devices corresponding to each edge computing node.

[0022] As a further solution of the present invention: the step of transmitting the edge processed data to the cloud for operating condition design and constraint fusion calculation, and obtaining the corresponding distributed new energy operating demand load constraints and adjustable capacity load constraints under different operating conditions includes:

[0023] Use IoT technology to transmit the distributed renewable energy power generation, adjustable load demand, and distributed energy storage device status data corresponding to each edge computing node to the cloud;

[0024] Use the cloud to obtain the operating function requirements and performance targets corresponding to each edge computing node, and design the operating conditions of the corresponding edge computing nodes based on the operating function requirements and performance targets corresponding to each edge computing node, so as to generate different operating condition design scenarios corresponding to each edge computing node;

[0025] Based on the different operating condition design scenarios corresponding to each edge computing node, the distributed renewable energy power generation, adjustable load demand and distributed energy storage device status data corresponding to each edge computing node are divided into operating condition scenario time periods to obtain the power generation, load demand and energy storage device status of each edge computing node in the corresponding time period under different operating conditions;

[0026] The operation demand load constraint fusion calculation is performed on the power generation power and energy storage device status of each edge computing node in the corresponding time period under different operating conditions, so as to obtain the distributed new energy operation demand load constraint corresponding to each edge computing node under different operating conditions;

[0027] The adjustable capacity load constraint fusion calculation is performed on the power generation power and load demand of each edge computing node in the corresponding time period under different operating conditions to obtain the adjustable capacity load constraint corresponding to each edge computing node under different operating conditions.

[0028] As a further solution of the present invention: the steps of performing an operation demand load constraint fusion calculation on the power generation power and energy storage device status of each edge computing node in a corresponding time period under different operating conditions to obtain the distributed new energy operation demand load constraint corresponding to each edge computing node under different operating conditions include:

[0029] Obtain the environmental condition change data corresponding to each edge computing node, and based on the environmental condition change data corresponding to each edge computing node, perform environmental seasonal impact assessment and analysis on the corresponding power generation power to obtain the environmental seasonal impact factor corresponding to each edge computing node;

[0030] Based on the environmental seasonal impact factor corresponding to each edge computing node, the power generation power of each edge computing node in the corresponding time period under different operating conditions is updated and calculated to obtain the updated power generation power of each edge computing node in the corresponding time period under different operating conditions;

[0031] Obtain the dynamic impact factor of the energy storage efficiency in the corresponding time period of each edge computing node, and perform device status update calculation on the energy storage device status of each edge computing node in the corresponding time period under different operating conditions based on the dynamic impact factor of the energy storage efficiency in the corresponding time period of each edge computing node, and obtain the updated status of the energy storage device of each edge computing node in the corresponding time period under different operating conditions;

[0032] The operation demand load constraint calculation formula is used to perform a fusion calculation of the updated power generation power and the updated status of the energy storage equipment of each edge computing node in the corresponding time period under different operating conditions, so as to obtain the distributed new energy operation demand load constraints corresponding to each edge computing node under different operating conditions.

[0033] As a further solution of the present invention: the adjustable capacity load constraint fusion calculation is performed through the adjustable capacity load constraint fusion calculation formula, and the adjustable capacity load constraint fusion calculation formula is specifically:

[0034] A i (t, C) = D i (t, C)-P g，i (t,C)

[0035] D i (t, C) = D b，i +D s，i (t,C)+Dr，i (t,C)

[0036] A min，i ≤A i (t,C)≤A max，i

[0037] In the formula, A i (t, C) is the adjustable capacity load constraint of the ith edge computing node in the corresponding time period t under the operating condition C, P g，i (t, C) is the power generation of the ith edge computing node in the corresponding time period t under the operating condition C, D i (t, C) is the load demand of the ith edge computing node in the corresponding time period t under the operating condition C, D b，i is the adjustable basic load corresponding to the i-th edge computing node, D s，i (t, C) is the seasonal load fluctuation of the ith edge computing node in the corresponding time period t under the operating condition C, D r，i (t, C) is the random load fluctuation of the ith edge computing node in the corresponding time period t under the operating condition C, A min，i is the minimum load of the adjustable capacity corresponding to the i-th edge computing node, A max，i is the maximum load of the adjustable capacity corresponding to the i-th edge computing node.

[0038] As a further solution of the present invention: the risk assessment analysis is calculated by a distributed new energy risk level calculation formula, and the distributed new energy risk level calculation formula is specifically:

[0039]

[0040] In the formula, R i (t, C) is the distributed new energy risk level of the ith edge computing node in the corresponding time period t under the operating condition C, L i (t, C) is the distributed renewable energy operation demand load constraint of the ith edge computing node under the operating condition C in the corresponding time period t, U(C) is the external uncertainty factor under the operating condition C, β is the uncertainty risk impact weight coefficient, A i (t, C) is the adjustable capacity load constraint of the i-th edge computing node in the corresponding time period t under the operating condition C, and ε is a non-zero adjustment constant.

[0041] As a further solution of the present invention: according to the risk level of new energy, the coordinated dispatching optimization of the consumption between each grid sub-node in the main distribution network of the power system is performed, and the coordinated dispatching optimization strategy of the consumption of the main distribution network of the power system is generated to perform the steps of coordinated dispatching of distributed new energy consumption between each grid sub-node of the corresponding main distribution network, including:

[0042] According to the distributed renewable energy risk level of each grid sub-node under different operating conditions, the consumption and dispatching demand of each grid sub-node in the main distribution network of the power system is predicted and analyzed, and the distributed renewable energy consumption and dispatching demand of each grid sub-node under different operating conditions is obtained;

[0043] Based on the distributed renewable energy consumption dispatching requirements of each grid sub-node under different operating conditions, the corresponding grid sub-nodes in the main distribution network of the power system are analyzed for consumption optimization targets, so as to obtain the distributed renewable energy consumption optimization targets corresponding to each grid sub-node under different operating conditions;

[0044] Based on the distributed new energy consumption optimization targets corresponding to each grid sub-node under different operating conditions, the consumption coordinated dispatch optimization is carried out between each grid sub-node in the main distribution network of the power system, and the power system main distribution network consumption coordinated dispatch optimization strategy is generated to execute the distributed new energy consumption coordinated dispatch work between each grid sub-node of the corresponding main distribution network.

[0045] As a further solution of the present invention: the updated power generation formula is:

[0046] P i (t, C) = P g，i (t, C)*η g，i (t,C)*S i (t);

[0047] P min，i ≤P i (t,C)≤P max，i ;

[0048] Where Pi(t, C) is the updated power generation of the ith edge computing node in the corresponding time period t under the operating condition C, P g，i (t, C) is the power generation of the ith edge computing node in the corresponding time period t under the operating condition C, η g，i (t, C) is the power generation efficiency of the i-th edge computing node in the corresponding time period t under the operating condition C, S i (t) is the environmental seasonal impact factor of the i-th edge computing node in the corresponding time period t, P min，i is the minimum power generation corresponding to the i-th edge computing node, P max，i is the maximum power generation corresponding to the i-th edge computing node.

[0049] As a further solution of the present invention: the operation demand load constraint calculation formula is specifically:

[0050] L i (t, C) = E i (t,C)-(Pi (t,C)+P d，i (t, C));

[0051] Where, L i (t, C) is the distributed renewable energy operation demand load constraint of the ith edge computing node in the corresponding time period t under the operating condition C, E i (t, C) is the updated state of the energy storage device of the ith edge computing node in the corresponding time period t under the operating condition C, Pi(t, C) is the updated power generation power of the ith edge computing node in the corresponding time period t under the operating condition C, P d，i (t, C) is the discharge power of the energy storage device of the i-th edge computing node in the corresponding time period t under the operating condition C.

[0052] Compared with the prior art, the beneficial effect of the present invention lies in: the distributed new energy consumption scheduling optimization method for power grid based on cloud-edge fusion computing, by configuring edge devices at each power grid sub-node and introducing edge computing nodes to perform edge data processing on distributed new energy power generation data and adjustable load operation status data, to obtain the corresponding distributed new energy power generation power, adjustable load demand and distributed energy storage equipment status data. This process can generate accurate power generation and load status information in real time, supporting managers to make more informed decisions in power dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0054] Figure 1 It is a schematic diagram of the steps of the method for optimizing the dispatching of distributed renewable energy consumption in power grids based on cloud-edge fusion computing of the present invention.

[0055] Figure 2 for Figure 1 Detailed step flow chart of step S1 in FIG.

[0056] Figure 3 for Figure 2 Detailed step flow chart of step S14 in FIG.

[0057] Figure 4 for Figure 1 Detailed step flow chart of step S2 in FIG.

[0058] Figure 5 for Figure 4 Detailed step flow chart of step S24 in FIG.

[0059] Figure 6 for Figure 1 Detailed step flow chart of step S4 in FIG. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0063] Furthermore, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] Example 1

[0065] like Figures 1 to 5 As shown, the present invention provides a technical solution: a method for optimizing the consumption and dispatching of distributed new energy in power grids based on cloud-edge fusion computing, comprising the steps of: S1: edge processing of real-time power generation and adjustable load operation status data acquired through edge devices; S2: transmitting the edge-processed data to the cloud for operation condition design and constraint fusion calculation, and obtaining the corresponding distributed new energy operation demand load constraints and adjustable capacity load constraints under different operation conditions; S3: performing risk assessment and analysis on the corresponding power grid sub-nodes in the main distribution network of the power system based on the load constraints and the adjustable capacity load constraints, and obtaining the corresponding distributed new energy risk level of each power grid sub-node under different operation conditions; S4: optimizing the consumption and coordinated dispatching between each power grid sub-node in the main distribution network of the power system according to the new energy risk level, and generating a power system main distribution network consumption and coordinated dispatching optimization strategy to execute the distributed new energy consumption and coordinated dispatching work between each power grid sub-node of the corresponding main distribution network.

[0066] Specifically, S1: obtain the real-time power generation and adjustable load operation status data through edge devices for edge processing, and obtain the distributed new energy power generation data and adjustable load operation status data corresponding to each grid sub-node in the main distribution network of the power system in real time through distributed sensors (sensors include current sensors, voltage sensors, power factor sensors and frequency sensors, etc.) and monitoring equipment (monitoring equipment includes solar power generation monitoring equipment, wind power generation monitoring equipment, environmental and meteorological monitoring equipment and energy storage monitoring equipment); introduce edge computing nodes by configuring edge devices at each grid sub-node (the edge computing node is to configure corresponding edge devices at each grid sub-node so that the grid sub-node is marked as an edge computing node, that is, the grid sub-node is changed into an edge computing node, so that its data transmission process has the function of edge computing to reduce transmission delay) The distributed new energy power generation data and the adjustable load operation status data are processed at the edge (that is, the corresponding power generation power, load demand and energy storage device status are calculated according to the corresponding new energy power generation data and load operation status), and the distributed new energy power generation power, adjustable load demand and distributed energy storage device status data corresponding to each edge computing node are obtained.

[0067] In an embodiment of the present invention, distributed sensors and monitoring equipment are deployed in the main distribution network of the power system to achieve real-time data acquisition of each grid sub-node. These sensors are capable of monitoring the operating status of the adjustable system, including measuring current, voltage, power factor and frequency, etc. At the same time, the monitoring equipment is also equipped to obtain distributed renewable energy (such as solar energy, wind energy, etc.) power generation data, including the specific duration of power generation, the amount of power generation and the power generation environment information, etc. During the data collection process, data compression and encryption transmission technology are adopted to obtain the distributed renewable energy power generation data and adjustable load operating status data corresponding to each grid sub-node. By designing the configuration of edge devices for each grid sub-node in the main distribution network of the power system, each grid sub-node is evaluated, including its power load characteristics, existing infrastructure, communication capabilities, etc. Based on these evaluation results, an edge device configuration blueprint is designed, and based on the edge device configuration blueprint generated by the previous design, the functions of the edge computing nodes are integrated for each grid sub-node in the main distribution network of the power system, so as to ensure its smooth operation in the grid sub-node by assembling and debugging the designed edge devices, wherein each edge computing node includes a data processing unit, a storage unit and a communication interface with the main distribution network, and adopts embedded system technology to ensure that the edge computing node has real-time data processing capabilities. During the integrated design process, multiple functional tests and performance verifications are carried out to ensure that the equipment can operate stably and can respond to changes in the operating status of the power grid in a timely manner, so as to obtain the corresponding edge computing nodes in the main distribution network of the power system. Then, by using the previously integrated edge computing nodes, the distributed renewable energy power generation data and adjustable load operation status data acquired in real time are processed at the edge. Each edge computing node processes the collected data locally, and calculates the distributed renewable energy power generation power, adjustable load demand and distributed energy storage equipment status data through edge computing. Stream processing technology is used to achieve real-time data analysis and decision-making to ensure timely optimization of power grid operation. The data processing results will be used to support power dispatching, load balancing and energy storage management, and finally the distributed renewable energy power generation power, adjustable load demand and distributed energy storage equipment status data corresponding to each edge computing node are obtained.

[0068] Further, S2: using the Internet of Things technology to transmit the distributed renewable energy power generation, adjustable load demand and distributed energy storage device status data corresponding to each edge computing node to the cloud for operating condition design and constraint fusion calculation, so as to obtain the distributed renewable energy operating demand load constraint and adjustable capacity load constraint corresponding to each edge computing node under different operating conditions;

[0069] In an embodiment of the present invention, the distributed renewable energy power generation, adjustable load demand and distributed energy storage device status data obtained by edge computing of sensors and data acquisition devices deployed at each edge computing node are transmitted to the cloud in real time by using the Internet of Things technology. In a specific implementation, the edge node collects data using a wireless communication protocol (such as LoRa, Zigbee or NB-IoT) to ensure stable transmission of data in different network environments, and uses data encryption technology to ensure information security, ensuring that all data is not tampered with during transmission. The operating function requirements and performance targets corresponding to each edge computing node obtained by cloud analysis are designed for the corresponding edge computing node in combination with a specific algorithm model (such as a rule-based decision tree or optimization algorithm) to design and generate different operating condition design scenarios, which include maximum power generation capacity, minimum load demand and optimal energy storage state, and the distributed renewable energy power generation, adjustable load demand and distributed energy storage device status data corresponding to each edge computing node are divided into time periods for operating condition scenarios, so as to analyze the environmental changes and load fluctuations of each edge computing node in different time periods, and use the time series analysis method ... The operating conditions of the power generation power, adjustable load demand and energy storage device status of the edge computing node are classified to form a time period data set under the corresponding operating conditions, including the power generation power, adjustable load demand and energy storage device status. The power generation power and energy storage device status of each edge computing node under different operating conditions are fused and calculated. The power generation power and energy storage status of each edge node are constrained by linear programming method, and an optimization model is constructed to solve the power load constraints that meet the operating requirements of each node. The model is solved by Matlab or optimization software to obtain the demand load constraints of each edge computing node under different operating conditions. Then, by performing a fusion calculation of the adjustable capacity load constraints for the power generation and load demand of each edge computing node under different operating conditions, a detailed analysis of the node load demand is performed by introducing a dynamic scheduling strategy and an adjustable load model, and advanced algorithms (such as genetic algorithms or particle swarm optimization) are used to model and evaluate the adjustable capacity of each node, and the range and restrictions of the adjustable load are determined. In the calculation process, the real-time data and prediction model are combined to optimize the adjustable capacity load of each node to achieve the purpose of ensuring the overall stability of the power system and optimizing scheduling, and finally the adjustable capacity load constraints corresponding to each edge computing node under different operating conditions are obtained.

[0070] Further, S3: based on the distributed renewable energy operation demand load constraints and adjustable capacity load constraints corresponding to each edge computing node under different operating conditions, a risk assessment analysis is performed on the corresponding grid sub-nodes in the main distribution network of the power system to obtain the distributed renewable energy risk level corresponding to each grid sub-node under different operating conditions;

[0071] In an embodiment of the present invention, a suitable risk assessment calculation formula is formed by combining the distributed new energy operation demand load constraints and adjustable capacity load constraints corresponding to different operating conditions, external uncertainty factors, uncertainty risk impact weight coefficients and related parameters obtained by previous analysis and calculation to evaluate and calculate the corresponding grid sub-nodes in the main distribution network of the power system, so as to quantitatively calculate the risk level of each grid sub-node, and finally obtain the distributed new energy risk level corresponding to each grid sub-node under different operating conditions.

[0072] Further, S4: according to the distributed renewable energy risk level corresponding to each grid sub-node under different operating conditions, the coordinated dispatching optimization of the consumption between each grid sub-node in the main distribution network of the power system is optimized, and the coordinated dispatching optimization strategy of the consumption of the main distribution network of the power system is generated to execute the coordinated dispatching of the consumption of distributed renewable energy between each grid sub-node of the corresponding main distribution network.

[0073] In an embodiment of the present invention, by combining the distributed new energy risk levels corresponding to each grid sub-node under different operating conditions obtained by previous quantitative calculation, the consumption and dispatching needs of each corresponding grid sub-node in the main distribution network of the power system are predicted and analyzed, so as to predict and analyze the consumption and dispatching needs of each grid sub-node under specific circumstances, and use machine learning algorithms to train historical data to accurately predict the new energy consumption needs of each grid sub-node, and by combining the distributed new energy consumption and dispatching needs obtained by previous prediction and analysis, the consumption optimization targets of the corresponding grid sub-nodes in the main distribution network of the power system are analyzed, so as to set the optimization objective function, such as minimizing energy loss or maximizing new energy utilization, by adopting optimization algorithms such as linear programming or integer programming, and at the same time When determining the constraints, including the maximum load, minimum power generation and power generation scheduling restrictions of the grid sub-nodes, and using optimization software such as MATLAB or GAMS, solve the optimization model to obtain the distributed new energy consumption optimization target of each grid sub-node under different operating conditions. Then, by combining the previously obtained optimization targets, the consumption coordinated scheduling optimization is performed between each grid sub-node in the main distribution network of the power system, so as to establish a coordinated scheduling model between the grid sub-nodes, define the power exchange and power balance relationship between the sub-nodes, and use a coordinated scheduling algorithm, such as a distributed algorithm or an integrated algorithm, to ensure that each sub-node achieves the optimal power allocation while meeting the consumption optimization target. Through simulation software such as PSS / E or DIgSILENT PowerFactory, simulate the implementation effect of coordinated scheduling, adjust the scheduling strategy, ensure the overall safety and stability of the system, thereby optimizing and generating the consumption coordinated scheduling optimization strategy of the main distribution network of the power system, guiding the actual distributed new energy consumption coordinated scheduling work, and performing corresponding execution and monitoring, and finally executing the distributed new energy consumption coordinated scheduling work between each grid sub-node of the corresponding main distribution network.

[0074] Example 2

[0075] As attached Figure 2 As shown, different from the previous embodiment, this embodiment further records the steps of edge processing the real-time power generation and adjustable load operation status data through the edge device, which are as follows:

[0076] S11: Real-time acquisition of distributed renewable energy generation data and adjustable load operation status data corresponding to each grid sub-node in the main distribution network of the power system through distributed sensors and monitoring equipment;

[0077] In an embodiment of the present invention, distributed sensors and monitoring equipment are deployed in the main distribution network of the power system to achieve real-time data acquisition of each grid sub-node. These sensors are capable of monitoring the operating status of the adjustable system, including measuring current, voltage, power factor and frequency, etc. At the same time, the monitoring equipment is also equipped to obtain distributed renewable energy (such as solar energy, wind energy, etc.) power generation data, including the specific duration of power generation, the amount of power generation and the power generation environment information, etc. During the data collection process, data compression and encryption transmission technology are adopted to improve the transmission efficiency and security of the network, and finally the distributed renewable energy power generation data and adjustable load operating status data corresponding to each grid sub-node are obtained.

[0078] S12: By performing edge device configuration design for each grid sub-node in the main distribution network of the power system (edge ​​devices such as smart meters, power quality monitors, etc.), a blueprint for edge device configuration design corresponding to each grid sub-node is generated;

[0079] In an embodiment of the present invention, the configuration design of the edge device is performed on each grid sub-node in the main distribution network of the power system, and each grid sub-node is evaluated, including its power load characteristics, existing infrastructure, communication capabilities, etc. Based on these evaluation results, an edge device configuration blueprint is designed. This blueprint lists in detail the type, quantity and functional modules of the required edge devices, including a data acquisition unit, a computing unit and a communication unit, that is, 4 smart meters, 2 power quality monitors, and 2 environmental sensors are configured in the data acquisition unit, 1 embedded computing device is configured in the computing unit, and 2 LoRa networks (for remote sensor connection) and 1 wired Ethernet gateway (for connecting local devices to the central system) are configured in the communication unit. During the design process, the application software is used to draw tables to ensure the accuracy and operability of the design. The edge device configuration blueprint corresponding to each grid sub-node will form a detailed implementation plan, and finally the edge device configuration design blueprint corresponding to each grid sub-node is designed and generated.

[0080] The configuration blueprint is as follows:

[0081]

[0082]

[0083] S13: Based on the edge device configuration design blueprint corresponding to each grid sub-node, the edge computing node function is integrated for the corresponding grid sub-nodes in the main distribution network of the power system to obtain the corresponding edge computing nodes in the main distribution network of the power system;

[0084] In an embodiment of the present invention, by combining the edge device configuration design blueprint generated by the previous design, the functions of the edge computing nodes are integrated for each grid sub-node in the main distribution network of the power system, so as to assemble and debug the designed edge devices to ensure their smooth operation in the grid sub-nodes, wherein each edge computing node (the grid sub-nodes configured with edge devices are marked as edge computing nodes) includes a data processing unit, a storage unit and a communication interface with the main distribution network. Embedded system technology is used to ensure that the edge computing nodes have real-time data processing capabilities. During the integrated design process, multiple functional tests and performance verifications are performed to ensure that the equipment can operate stably and can respond to changes in the operating status of the power grid in a timely manner, and finally the corresponding edge computing nodes in the main distribution network of the power system are obtained.

[0085] S14: Use the corresponding edge computing nodes in the main distribution network of the power system to perform edge data processing on the distributed renewable energy power generation data and the adjustable load operation status data to obtain the distributed renewable energy power generation power, adjustable load demand and distributed energy storage equipment status data corresponding to each edge computing node.

[0086] In an embodiment of the present invention, edge data processing is performed on distributed renewable energy power generation data and adjustable load operation status data acquired in real time by using the corresponding edge computing nodes in the main distribution network of the power system previously integrated. Each edge computing node locally processes the collected data, and calculates the distributed renewable energy power generation power, adjustable load demand and status data of distributed energy storage equipment through the edge of the algorithm model. Stream processing technology is used to realize real-time analysis and decision-making of data to ensure timely optimization of power grid operation. The data processing results will be used to support power dispatching, load balancing and energy storage management, and finally the distributed renewable energy power generation power, adjustable load demand and distributed energy storage equipment status data corresponding to each edge computing node are obtained.

[0087] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 In the detailed step flow diagram of S14, in this embodiment, step S14 includes the following steps:

[0088] S141: Use each corresponding edge computing node in the main distribution network of the power system to monitor the power generation operation time of the distributed renewable energy power generation data, and obtain the distributed renewable energy power generation operation time corresponding to each edge computing node;

[0089] In an embodiment of the present invention, each edge computing node corresponding to the main distribution network of the power system obtained by the previously integrated configuration is used to monitor and obtain the power generation operation time of the distributed renewable energy power generation data obtained in real time in each node, so as to collect the operation time information of different types of renewable energy including photovoltaic power generation, wind power generation, etc. In the specific implementation, the real-time power generation data collected by each edge computing node is uploaded to the central processing unit through a wireless network by using sensors and data acquisition equipment, and the power generation operation time of each edge node is calculated by using a data processing algorithm, and a corresponding power generation operation time report is generated, so as to finally obtain the distributed renewable energy power generation operation time corresponding to each edge computing node.

[0090] S142: Calculate the edge power generation power of the distributed renewable energy power generation data according to the distributed renewable energy power generation operation time corresponding to each edge computing node and the power generation amount and environmental conditions during the operation time corresponding to the distributed renewable energy power generation data to obtain the distributed renewable energy power generation power corresponding to each edge computing node;

[0091] In an embodiment of the present invention, the power generation is calculated by combining the distributed renewable energy power generation operation time corresponding to each edge computing node obtained by previous monitoring with the power generation amount and environmental conditions corresponding to the distributed renewable energy power generation data during the operation time. In specific operations, the local processing capabilities of the edge computing nodes are used in combination with environmental conditions (such as temperature, humidity, wind speed, etc.) to perform correlation analysis between power generation amount and environmental conditions. The actual power generation of each node is accurately calculated using algorithms such as regression models or neural networks. These calculation results are stored in a local database and synchronized with a central server regularly to ensure the integrity and traceability of the data, and finally the distributed renewable energy power generation corresponding to each edge computing node is obtained.

[0092] S143: Performing edge load operation response analysis on the adjustable load operation status data using corresponding edge computing nodes in the main distribution network of the power system to obtain adjustable load operation response data corresponding to each edge computing node;

[0093] In an embodiment of the present invention, by using the corresponding edge computing nodes in the main distribution network of the power system obtained by the previous integrated configuration, the corresponding adjustable load operation status data is subjected to load operation response identification analysis, so as to utilize load monitoring sensors to collect the operation data of each device, and adopt data analysis tools, such as timing analysis method, to perform response analysis on the load changes. These analysis results show the load response characteristics of each edge node, provide basic data support for subsequent load demand inference, and finally obtain the adjustable load operation response data corresponding to each edge computing node.

[0094] S144: performing load operation demand inference analysis on the corresponding edge computing nodes in the main distribution network of the power system according to the adjustable load operation response data corresponding to each edge computing node, and obtaining the adjustable load demand corresponding to each edge computing node;

[0095] In an embodiment of the present invention, by combining the adjustable load operation response data corresponding to each edge computing node obtained by previous response analysis, the load operation demand of the adjustable system at the corresponding edge computing node in the main distribution network of the power system is inferred and analyzed. In specific implementation, statistical analysis software is used to cross-compare historical load data and response data to establish a load demand prediction model. Each edge computing node derives the required adjustable load demand based on its specific load characteristics and response data. These demand data will be used for scheduling decisions, and finally the adjustable load demand corresponding to each edge computing node is obtained.

[0096] S145: Based on the distributed renewable energy power generation and adjustable load demand corresponding to each edge computing node, an energy storage status evaluation and analysis is performed on the energy storage devices in the grid sub-nodes corresponding to each edge computing node to obtain the distributed energy storage device status data corresponding to each edge computing node.

[0097] In an embodiment of the present invention, the energy storage device status is evaluated and analyzed by combining the distributed renewable energy power generation and adjustable load demand corresponding to each edge computing node obtained by the previous edge computing analysis to the energy storage device in the power grid sub-node corresponding to each edge computing node. In a specific implementation, each edge computing node uses an integrated monitoring system and combines the power generation and load demand calculated previously to perform energy storage status analysis through an algorithm model to analyze and obtain real-time status data such as the charging and discharging status and remaining power of the energy storage device (such as battery packs, flywheel energy storage, etc.). This analysis result will provide an important basis for the dispatch optimization of the power grid, ensure the optimal balance between renewable energy supply and load demand, and finally obtain the distributed energy storage device status data corresponding to each edge computing node.

[0098] Further, step S145 includes the following steps:

[0099] According to the distributed renewable energy power generation and adjustable load requirements corresponding to each edge computing node, the energy storage state constraint conditions of the energy storage devices in the grid sub-nodes corresponding to each edge computing node are designed to generate different power generation and load requirements corresponding to the energy storage devices of each edge computing node;

[0100] In an embodiment of the present invention, by collecting the distributed renewable energy power generation data and adjustable load demand information corresponding to each edge computing node, and for each edge computing node, using data analysis tools to perform detailed statistical analysis on the power generation and load demand, the power generation capacity and load fluctuation trend within a specific time period are identified, and then, based on these analysis results, energy storage state constraints are designed. Specifically, constraints such as maximum energy storage capacity, minimum discharge state, and charging time window can be defined to ensure the effective operation of energy storage equipment under different power generation and load conditions, and finally, different power generation and load demand conditions corresponding to each edge computing node energy storage device are designed and generated.

[0101] Preferably, based on the different power generation and load demand conditions corresponding to the energy storage devices of each edge computing node, the energy storage state evaluation index analysis is performed on the energy storage devices in the grid sub-nodes corresponding to each edge computing node to obtain the energy storage state evaluation index system corresponding to the energy storage devices of each edge computing node;

[0102] In an embodiment of the present invention, by combining the previously designed and generated energy storage devices of each edge computing node corresponding to different power generation and load demand conditions, the energy storage devices in the grid sub-nodes corresponding to each edge computing node are statistically analyzed for energy storage status evaluation indicators, so as to establish an energy storage status evaluation indicator system for the energy storage devices of each edge computing node, and by introducing data mining technology and statistical methods, the performance of each energy storage device under different power generation and load demand conditions is evaluated. In specific implementation, indicators such as energy storage efficiency, response time, number of cycles and failure rate can be selected as key parameters in the evaluation system, and simulation software is used to simulate and test the energy storage device under various conditions, collect data and calculate various indicators to form a quantitative evaluation system. The evaluation results will be used to guide the adjustment of the operation strategy of the energy storage device to ensure that it always maintains the best state under different load demands, and finally obtain the energy storage status evaluation indicator system corresponding to the energy storage devices of each edge computing node.

[0103] Preferably, based on the energy storage status evaluation index system corresponding to the energy storage devices of each edge computing node, the energy storage status evaluation and analysis is performed on the energy storage devices in the power grid sub-nodes corresponding to each edge computing node to obtain the distributed energy storage device status data corresponding to each edge computing node.

[0104] In an embodiment of the present invention, a comprehensive status evaluation and analysis of the energy storage devices of each edge computing node is performed by combining the energy storage status evaluation index system corresponding to the energy storage devices of each edge computing node obtained by previous evaluation and analysis. Specifically, the real-time data of the energy storage devices during operation is first collected and input into the evaluation model. The status data of each energy storage device in different time periods is obtained through algorithm calculation, including information such as current power, available capacity, and charging and discharging status. These data are then visualized and a status report is generated to facilitate relevant personnel to quickly understand and make decisions on the operating status of the energy storage devices, so that they are more in line with the overall needs of the power grid, ensure the efficient absorption and utilization of new energy, and finally obtain the status data of the distributed energy storage devices corresponding to each edge computing node.

[0105] Example 3

[0106] As attached Figure 4 As shown, different from the previous embodiment, this embodiment further records that S2 transmits the edge processed data to the cloud for operating condition design and constraint fusion calculation, and obtains the corresponding distributed new energy operating demand load constraints and adjustable capacity load constraints under different operating conditions. The steps include:

[0107] S21: Use the Internet of Things technology to transmit the distributed renewable energy power generation, adjustable load demand, and distributed energy storage device status data corresponding to each edge computing node to the cloud;

[0108] In an embodiment of the present invention, the distributed renewable energy power generation power, adjustable load demand and status data of distributed energy storage equipment obtained by edge computing of sensors and data acquisition devices deployed at each edge computing node are transmitted to the cloud in real time by using the Internet of Things technology. In a specific implementation, the edge node collects data using wireless communication protocols (such as LoRa, Zigbee or NB-IoT) to ensure stable transmission of data in different network environments, and uses data encryption technology to ensure information security, ensuring that all data is not tampered with during transmission.

[0109] S22: using the cloud to obtain the operating function requirements and performance targets corresponding to each edge computing node, and designing the operating conditions of the corresponding edge computing nodes based on the operating function requirements and performance targets corresponding to each edge computing node, so as to generate different operating condition design scenarios corresponding to each edge computing node;

[0110] In an embodiment of the present invention, the operating function requirements and performance targets corresponding to each edge computing node are obtained by using cloud-based analysis, and the operating conditions of the corresponding edge computing nodes are designed in combination with a specific algorithm model (such as a rule-based decision tree or optimization algorithm). Taking a certain edge node as an example, the power generation capacity and load demand of the node are first evaluated based on historical data and prediction models, and then different operating condition design scenarios are generated through algorithms. These scenarios include maximum power generation capacity, minimum load demand and optimal energy storage status, forming a complete set of operating condition design solutions, and finally designing and generating different operating condition design scenarios corresponding to each edge computing node.

[0111] S23: Based on the different operating condition design scenarios corresponding to each edge computing node, the distributed new energy power generation, adjustable load demand and distributed energy storage device status data corresponding to each edge computing node are divided into operating condition scenario time periods to obtain the power generation, load demand and energy storage device status of each edge computing node in the corresponding time period under different operating conditions;

[0112] In an embodiment of the present invention, by combining the different operating condition design scenarios corresponding to each edge computing node generated by the previous design, the distributed new energy power generation power, adjustable load demand and distributed energy storage device status data corresponding to each edge computing node are divided into time periods of operating condition scenarios, so as to analyze the environmental changes and load fluctuations of each edge computing node in different time periods, and use the time series analysis method to classify the operating conditions of each node's power generation power, adjustable load demand and energy storage device status. In specific operations, data mining tools (such as the Pandas library in Python) are used to aggregate the data by hour, day or week, so as to form a time period data set under the corresponding operating conditions, clarify the specific operating conditions in each time period, and finally obtain the power generation power, load demand and energy storage device status of each edge computing node in the corresponding time period under different operating conditions.

[0113] S24: Performing a fusion calculation of the operation demand load constraints on the power generation power and energy storage device status of each edge computing node in a corresponding time period under different operating conditions, so as to obtain the distributed new energy operation demand load constraints corresponding to each edge computing node under different operating conditions;

[0114] In an embodiment of the present invention, by performing a fusion calculation of the demand load constraints on the power generation power and energy storage device status of each edge computing node under different operating conditions, a linear programming method is used to set constraints on the power generation power and energy storage status of each edge node, and an optimization model is constructed to solve the power load constraints that meet the operating requirements of each node. The model is solved by Matlab or optimization software to obtain the demand load constraints under different operating conditions, thereby ensuring the stability and security of the power supply, and finally obtaining the distributed new energy operation demand load constraints corresponding to each edge computing node under different operating conditions.

[0115] S25: Perform an adjustable capacity load constraint fusion calculation on the power generation power and load demand of each edge computing node in the corresponding time period under different operating conditions to obtain the adjustable capacity load constraints corresponding to each edge computing node under different operating conditions.

[0116] In an embodiment of the present invention, by performing a fusion calculation of the adjustable capacity load constraints for the power generation power and load demand of each edge computing node under different operating conditions, a dynamic scheduling strategy and an adjustable load model are introduced to perform a detailed analysis of the load demand of the node, and advanced algorithms (such as genetic algorithms or particle swarm optimization) are used to model and evaluate the adjustable capacity of each node, and the range and restriction conditions of the adjustable load are determined. In the calculation process, the real-time data and the prediction model are combined to optimize the adjustable capacity load of each node, so as to achieve the purpose of ensuring the overall stability of the power system and optimizing scheduling, and finally obtain the adjustable capacity load constraints corresponding to each edge computing node under different operating conditions.

[0117] Further, such as Figure 5 As shown, step S24 includes the following steps:

[0118] S241: Obtain environmental condition change data corresponding to each edge computing node, and perform environmental seasonal impact assessment and analysis on the corresponding power generation power based on the environmental condition change data corresponding to each edge computing node to obtain the environmental seasonal impact factor corresponding to each edge computing node;

[0119] In an embodiment of the present invention, environmental condition change data, including meteorological parameters such as temperature, humidity, wind speed, and light intensity, are obtained at each edge computing node in the main distribution network of the power system. These data are collected in real time by sensors installed at each grid sub-node, and the Internet of Things (IoT) technology is used to transmit the data to the edge computing platform. Based on the collected environmental data, a machine learning model (such as a random forest or a support vector machine) is used to analyze the impact of environmental condition parameters on power generation. The model is trained with historical power generation data and corresponding environmental condition data to obtain the environmental seasonal impact factor of each node. The generated impact factor is used to quantify the degree of impact of different seasons and different environmental conditions on the power generation of each node, and finally the environmental seasonal impact factor corresponding to each edge computing node is obtained.

[0120] S242: Based on the environmental seasonal impact factor corresponding to each edge computing node, the power generation power of each edge computing node in the corresponding time period under different operating conditions is updated and calculated to obtain the updated power generation power of each edge computing node in the corresponding time period under different operating conditions:

[0121] P i (t, C) = P g，i (t, C)*η g，i (t,C)*S i (t);

[0122] P min,i ≤P i (t,C)≤P max，i ;

[0123] Where Pi(t, C) is the updated power generation of the ith edge computing node in the corresponding time period t under the operating condition C, P g，i (t, C) is the power generation of the ith edge computing node in the corresponding time period t under the operating condition C, η g，i (t, C) is the power generation efficiency of the i-th edge computing node in the corresponding time period t under the operating condition C, S i (t) is the environmental seasonal impact factor of the i-th edge computing node in the corresponding time period t, P min,i is the minimum power generation corresponding to the i-th edge computing node, P max，i is the maximum power generation corresponding to the i-th edge computing node;

[0124] In an embodiment of the present invention, the power generation of each edge computing node under different operating conditions is updated and calculated by combining the environmental seasonal impact factors corresponding to each edge computing node obtained by previous evaluation and analysis to determine the power generation and power generation efficiency in different operating conditions and time periods. This can be obtained through real-time monitoring equipment (such as smart meters), and the impact factors are combined with the power generation and power generation efficiency to obtain new power generation values ​​through weighted calculation methods. For example, when environmental conditions change, the power generation is adjusted in real time, taking into account the characteristics of renewable energy such as wind energy and solar energy, and the updated power generation is calculated and output through the algorithm module of the edge computing node. This process ensures that the power system can respond quickly to environmental changes, thereby optimizing power generation efficiency, and ultimately obtaining the updated power generation of each edge computing node in the corresponding time period under different operating conditions.

[0125] S243: Obtain the dynamic impact factor of the energy storage efficiency in the corresponding time period of each edge computing node, and perform device status update calculation on the energy storage device status of each edge computing node in the corresponding time period under different operating conditions based on the dynamic impact factor of the energy storage efficiency in the corresponding time period of each edge computing node, and obtain the updated state of the energy storage device of each edge computing node in the corresponding time period under different operating conditions:

[0126]

[0127] 0≤E i (t,C)≤E max，i ;P c，i (t,C)≤P c，max，i ;P d，i (t,C)≤P d，max，i

[0128] Among them, E i (t, C) is the updated state of the energy storage device of the ith edge computing node in the corresponding time period t under the operating condition C, E i (t-1) is the energy storage device status of the i-th edge computing node in the corresponding time period t-1, P c，i (t, C) is the charging power of the energy storage device in the corresponding time period t under the operating condition C of the i-th edge computing node, η c，i is the charging efficiency corresponding to the i-th edge computing node, P d，i (t, C) is the energy storage device discharge power of the i-th edge computing node in the corresponding time period t under the operating condition C, η d，i is the discharge efficiency corresponding to the i-th edge computing node, F i (t, C) is the dynamic impact factor of energy storage efficiency of the ith edge computing node in the corresponding time period t under operating condition C, E max，iis the maximum constraint state of the energy storage device corresponding to the i-th edge computing node, P c，max，i is the maximum charging power of the energy storage device corresponding to the i-th edge computing node, P d，max，i is the maximum discharge power of the energy storage device corresponding to the i-th edge computing node;

[0129] In an embodiment of the present invention, by collecting the dynamic influencing factors of energy storage efficiency of each edge computing node within a specific time period, these factors are provided by the performance monitoring module of the energy storage system, covering charging and discharging efficiency, cycle life, temperature influence, etc., and by using the dynamic monitoring system to obtain device status information in real time, including the remaining power and health status of the energy storage device, then, based on the dynamic influencing factors of energy storage efficiency, a mathematical model (such as a linear regression model) is used to update the calculation of the energy storage device status. The specific method is to apply the dynamic influencing factors of energy storage efficiency to the current state of the energy storage device, adjust the effective energy storage capacity of the device, and ensure that the energy storage device can efficiently utilize and store energy under different operating conditions, thereby supporting the stability and power supply capacity of the power grid, and finally update the calculation to obtain the updated status of the energy storage device of each edge computing node in the corresponding time period under different operating conditions.

[0130] S244: Use the operation demand load constraint calculation formula to perform operation demand load constraint fusion calculation on the updated power generation power and energy storage device update status of each edge computing node in the corresponding time period under different operating conditions, so as to obtain the distributed new energy operation demand load constraints corresponding to each edge computing node under different operating conditions.

[0131] In an embodiment of the present invention, by using the corresponding operation demand load constraint calculation formula, the updated power generation power in step S242 and the updated state of the energy storage device in step S243 are integrated and calculated to clarify the operation requirements of each edge computing node, including load characteristics and power supply capacity, and the power generation power and the state of the energy storage device are comprehensively evaluated through a mathematical model (such as a constraint optimization algorithm) to ensure that the power supply capacity can meet the required load. In this process, simulation tools (such as MATLAB / Simulink) are used to simulate and optimize the load constraints, and the operation status of each node is analyzed in real time to ensure that efficient new energy consumption scheduling can be achieved under different operating conditions. This integrated calculation result will provide a scientific basis for the scheduling optimization of the power grid and ensure the safe and stable operation of the power grid. Finally, the distributed new energy operation demand load constraints corresponding to each edge computing node under different operating conditions are obtained by fusion calculation.

[0132] Furthermore, the operation demand load constraint calculation formula is specifically as follows:

[0133] L i (t, C) = E i (t,C)-(Pi (t,C)+P d，i (t,C))

[0134] Where, L i (t, C) is the distributed renewable energy operation demand load constraint of the ith edge computing node in the corresponding time period t under the operating condition C, E i (t, C) is the updated state of the energy storage device of the ith edge computing node in the corresponding time period t under the operating condition C, P i (t, C) is the updated power generation of the ith edge computing node in the corresponding time period t under the operating condition C, P d，i (t, C) is the discharge power of the energy storage device of the i-th edge computing node in the corresponding time period t under the operating condition C.

[0135] The present invention obtains an operation demand load constraint calculation formula by using a specific mathematical model and after verification, which is used to perform operation demand load constraint fusion calculation on the updated power generation power and energy storage device update status of each edge computing node in the corresponding time period under different operating conditions. The formula fully considers the distributed new energy operation demand load constraint L of the i-th edge computing node in the corresponding time period t under the operating condition C. i (t, C), the energy storage device update state E of the i-th edge computing node in the corresponding time period t under the operating condition C i (t, C), the updated power generation P of the i-th edge computing node in the corresponding time period t under the operating condition C i (t, C), the energy storage device discharge power P of the i-th edge computing node in the corresponding time period t under the operating condition C d，i (t, C), where the power generated by the i-th edge computing node in the corresponding time period t under the operating condition C is P g，i (t, C), the power generation efficiency η of the i-th edge computing node in the corresponding time period t under the operating condition C g，i (t, C), the environmental seasonal impact factor S of the i-th edge computing node in the corresponding time period t i (t), the minimum power generation P corresponding to the i-th edge computing node min，i And the maximum power generation power P corresponding to the i-th edge computing node max，i It constitutes a functional constraint relationship for updating the power generation Pi(t, C) of the i-th edge computing node in the corresponding time period t under the operating condition C:

[0136] P i (t, C) = P g，i (t, C)*η g，i (t,C)*S i (t)

[0137] P min，i ≤P i (t,C)≤P max，i

[0138] The energy storage device state E of the i-th edge computing node in the corresponding time period t-1 is also calculated. i (t-1), the energy storage device charging power P of the i-th edge computing node in the corresponding time period t under the operating condition C c，i (t, C), the charging efficiency η corresponding to the i-th edge computing node c，i , the energy storage device discharge power P of the i-th edge computing node in the corresponding time period t under the operating condition C d，i (t, C), the discharge efficiency η corresponding to the i-th edge computing node d，i , the dynamic impact factor F of energy storage efficiency of the i-th edge computing node in the corresponding time period t under the operating condition C i (t, C), the maximum constraint state E of the energy storage device corresponding to the i-th edge computing node max，i , the maximum charging power P of the energy storage device corresponding to the i-th edge computing node c，max，i And the maximum discharge power P of the energy storage device corresponding to the i-th edge computing node d，max，i It constitutes an energy storage device update state E of the i-th edge computing node in the corresponding time period t under the operating condition C. i Function constraint relationship of (t, C):

[0139]

[0140] 0≤E i (t,C)≤E max，i ;P c，i (t,C)≤P c，max，i ;P d，i (t,C)≤P d，max，i

[0141] According to the distributed new energy operation demand load constraint L of the i-th edge computing node in the corresponding time period t under the operating condition C i The correlation between (t, C) and the above parameters constitutes a functional relationship E i (t,C)-(P i (t,C)+P d，i(t, C)), this calculation formula enables each edge computing node to monitor the balance between power generation, energy storage status and discharge demand in real time, which helps to optimize the distribution and use of electricity. By calculating the relationship between the updated state of the energy storage device and the power generation and discharge power, the performance and current operating status of the energy storage device can be fully reflected. This formula helps to quantify the load demand in a specific time period, so that power dispatch and load management can be better carried out to ensure that energy supply can meet the demand. In addition, based on different operating conditions, the formula can help identify load changes under specific conditions, thereby optimizing operating strategies and energy efficiency. This formula is not only applicable to existing edge computing nodes, but can also be extended to other similar distributed energy systems, and has a wide range of application potential. By more accurately calculating and managing energy storage and power generation, the utilization rate of renewable energy can be effectively improved, waste can be reduced, and sustainable development can be promoted. In summary, the operation demand load constraint calculation formula promotes the efficient operation and resource management of edge computing nodes under different operating conditions by comprehensively considering energy storage status, power generation capacity and load demand, thereby improving the reliability and economy of the overall power system.

[0142] Furthermore, the adjustable capacity load constraint fusion calculation in step S25 is performed through the adjustable capacity load constraint fusion calculation formula, and the adjustable capacity load constraint fusion calculation formula is specifically:

[0143] A i (t,C)=D i (t,C)-P g,i (t,C)

[0144] D i (t,C)=D b,i +D s,i (t,C)+D r,i (t,C)

[0145] A min,i ≤A i (t,C)≤A max,i

[0146] In the formula, A i (t,C) is the adjustable capacity load constraint of the ith edge computing node in the corresponding time period t under the operating condition C, P g,i (t,C) is the power generation of the ith edge computing node in the corresponding time period t under the operating condition C, D i (t,C) is the load demand of the ith edge computing node in the corresponding time period t under the operating condition C, D b,i is the adjustable basic load corresponding to the i-th edge computing node, D s,i(t,C) is the seasonal load fluctuation of the ith edge computing node in the corresponding time period t under the operating condition C, D r,i (t,C) is the random load fluctuation of the ith edge computing node in the corresponding time period t under the operating condition C. min,i is the minimum load of the adjustable capacity corresponding to the i-th edge computing node, A max,i is the maximum load of the adjustable capacity corresponding to the i-th edge computing node.

[0147] The present invention obtains an adjustable capacity load constraint fusion calculation formula by using a specific mathematical model and verification, which is used to perform adjustable capacity load constraint fusion calculation on the power generation power and load demand of each edge computing node in the corresponding time period under different operating conditions. The formula fully considers the adjustable capacity load constraint A of the i-th edge computing node in the corresponding time period t under the operating condition C. i (t,C), the power generation P of the i-th edge computing node in the corresponding time period t under the operating condition C g,i (t,C), the load demand D of the i-th edge computing node in the corresponding time period t under the operating condition C i (t,C), the adjustable basic load D corresponding to the i-th edge computing node b,i , the seasonal load fluctuation D of the i-th edge computing node in the corresponding time period t under the operating condition C s,i (t,C), the random load fluctuation D of the ith edge computing node in the corresponding time period t under the operating condition C r,i (t,C), the minimum load A of the adjustable capacity corresponding to the i-th edge computing node min,i , the maximum load A of the adjustable capacity corresponding to the i-th edge computing node max,i , where the adjustable basic load D corresponding to the i-th edge computing node b,i , the seasonal load fluctuation D of the i-th edge computing node in the corresponding time period t under the operating condition C s,i (t, C) and the random load fluctuation D of the i-th edge computing node in the corresponding time period t under the operating condition C r，i (t, C) constitutes a load demand D of the i-th edge computing node in the corresponding time period t under the operating condition C. i The functional relationship D of (t, C) b，i +D s，i (t,C)+D r，i (t, C), according to the adjustable capacity load constraint A of the i-th edge computing node in the corresponding time period t under the operating condition C i The mutual relationship between (t, C) and the above parameters constitutes a functional constraint relationship:

[0148] A min，i ≤A i (t, C) = D i (t, C)-P g，i (t,C)≤A max，i

[0149] The adjustable capacity load constraint fusion calculation formula accurately calculates the adjustable capacity load constraint, so that the edge computing node can more effectively match the power generation and load demand, thereby reducing energy waste and improving the overall power system operation efficiency. Seasonal and random load fluctuations are introduced in the formula, so that the edge computing node can flexibly respond to load changes. This dynamic adjustment capability helps to achieve optimal energy management and distribution under different operating conditions. Secondly, through in-depth analysis of power generation and load demand, better load balance can be achieved to avoid potential risks caused by supply and demand imbalance, such as power shortage or surplus. The adjustable basic load and its upper and lower limits in the formula provide system operators with more strategic options. According to different needs and operating conditions, the operation strategy can be quickly adjusted to meet the real-time load demand. At the same time, by real-time monitoring and adjusting the load constraints of edge computing nodes, the system instability caused by sudden load fluctuations can be reduced to ensure the safe and reliable operation of the power grid. By combining the data analysis capabilities of edge computing and cloud, more scientific decisions can be made based on historical and real-time data. This data-driven approach can improve the intelligence level of the overall system. Overall, the adjustable capacity load constraint fusion calculation formula has significant advantages in improving the operating efficiency, flexibility and stability of new energy systems, and provides strong support for the combination of smart grids and edge computing. This formula can realize the adjustable capacity load constraint fusion calculation process of the power generation power and load demand of each edge computing node in the corresponding time period under different operating conditions, thereby improving the accuracy and applicability of the adjustable capacity load constraint fusion calculation formula.

[0150] Furthermore, the risk assessment analysis in step S3 is calculated by a distributed new energy risk level calculation formula, and the distributed new energy risk level calculation formula is specifically:

[0151]

[0152] In the formula, R i (t, C) is the distributed new energy risk level of the ith edge computing node in the corresponding time period t under the operating condition C, L i (t, C) is the distributed renewable energy operation demand load constraint of the ith edge computing node under the operating condition C in the corresponding time period t, U(C) is the external uncertainty factor under the operating condition C, β is the uncertainty risk impact weight coefficient, A i(t, C) is the adjustable capacity load constraint of the i-th edge computing node in the corresponding time period t under the operating condition C, and ε is a non-zero adjustment constant.

[0153] The present invention obtains a distributed new energy risk level calculation formula by using a specific mathematical model and after verification, which is used to perform risk assessment and analysis on the corresponding power grid sub-nodes in the main distribution network of the power system. The distributed new energy risk level calculation formula can quantify the new energy risk level of different power grid sub-nodes under specific conditions by combining the operating demand load constraint and the adjustable capacity load constraint. This quantification provides a scientific basis for decision-making and helps to identify high-risk areas. By introducing external uncertainty factors and uncertainty risk impact weight coefficients, the formula can adapt to different operating conditions, reflect system state changes in real time, and ensure that risk management strategies can be flexibly adjusted when facing different environmental factors. The risk assessment results can be directly used for consumption and coordinated scheduling optimization. By understanding the risk level of each power grid sub-node, power scheduling can be arranged more reasonably to reduce the imbalance between supply and demand caused by new energy fluctuations. In addition, in the main distribution network of the power system, scheduling optimization is performed according to the risk assessment results, which can effectively respond to emergencies (such as equipment failures, weather changes, etc.) and improve the resilience and reliability of the overall system. The real-time data provided by the edge computing node enables risk assessment to be based on the latest information, avoiding decision-making errors caused by lagging data. This real-time nature enables the power system to respond quickly to changes. In summary, through the application of this series of steps and formulas, not only an effective risk management and dispatch optimization solution is provided for distributed renewable energy in the main distribution network of the power system, but also a solid foundation is laid for the development of future smart grids. This comprehensive risk assessment and dispatch strategy will promote the sustainable, stable and efficient operation of the power system. Therefore, this formula fully considers the distributed renewable energy risk level R of the ith edge computing node in the corresponding time period t under the operating condition C. i (t,C), the distributed renewable energy operation demand load constraint L of the ith edge computing node in the corresponding time period t under the operating condition C i (t, C), the external uncertainty factor U(C) under operating condition C, the uncertainty risk impact weight coefficient β, the adjustable capacity load constraint A of the i-th edge computing node in the corresponding time period t under operating condition C i (t, C), a non-zero adjustment constant ε, based on the distributed new energy risk level R of the ith edge computing node in the corresponding time period t under the operating condition C i The correlation between (t,C) and the above parameters constitutes a functional relationship This formula can realize the risk assessment and analysis process of the corresponding grid sub-nodes in the main distribution network of the power system, thereby improving the accuracy and applicability of the distributed new energy risk level calculation formula.

[0154] Example 4

[0155] As attached Figure 6 As shown, different from the previous embodiment, this embodiment further records that step S4 performs risk assessment analysis on the corresponding grid sub-nodes in the main distribution network of the power system based on the load constraint and the adjustable capacity load constraint, and obtains the distributed new energy risk level corresponding to each grid sub-node under different operating conditions, including the following steps:

[0156] S41: According to the distributed renewable energy risk level of each grid sub-node under different operating conditions, the consumption and dispatching demand of each grid sub-node in the main distribution network of the power system is predicted and analyzed to obtain the distributed renewable energy consumption and dispatching demand of each grid sub-node under different operating conditions;

[0157] In an embodiment of the present invention, by combining the distributed new energy risk levels corresponding to each grid sub-node under different operating conditions obtained by previous quantitative calculation, the corresponding grid sub-nodes in the main distribution network of the power system are predicted and analyzed to predict and analyze the consumption and dispatching needs of each grid sub-node under specific circumstances, and the historical data is trained using a machine learning algorithm to accurately predict the new energy consumption demand of each grid sub-node, and the results are organized into a report to ensure that reliable input is provided for subsequent steps, and finally the distributed new energy consumption and dispatching needs corresponding to each grid sub-node under different operating conditions are obtained.

[0158] S42: Based on the distributed renewable energy consumption dispatching requirements corresponding to each grid sub-node under different operating conditions, the corresponding grid sub-nodes in the main distribution network of the power system are analyzed for consumption optimization targets, so as to obtain the distributed renewable energy consumption optimization targets corresponding to each grid sub-node under different operating conditions;

[0159] In an embodiment of the present invention, by combining the distributed new energy consumption and dispatching requirements corresponding to each grid sub-node under different operating conditions obtained by previous prediction and analysis, the corresponding grid sub-nodes in the main distribution network of the power system are analyzed for the consumption optimization targets, so as to set the optimization objective function by adopting optimization algorithms such as linear programming or integer programming, such as minimizing energy loss or maximizing new energy utilization. At the same time, the constraints, including the maximum load, minimum power generation and power generation dispatching restrictions of the grid sub-nodes, are determined, and the optimization model is solved by using optimization software such as MATLAB or GAMS to obtain the distributed new energy consumption optimization targets of each grid sub-node under different operating conditions. The analysis results are presented in a clear form to facilitate decision makers to formulate subsequent dispatching strategies, and finally the distributed new energy consumption optimization targets corresponding to each grid sub-node under different operating conditions are obtained.

[0160] S43: Based on the distributed new energy consumption optimization targets corresponding to each grid sub-node under different operating conditions, the consumption coordinated dispatch optimization is optimized between each grid sub-node in the main distribution network of the power system, and the power system main distribution network consumption coordinated dispatch optimization strategy is generated to execute the distributed new energy consumption coordinated dispatch work between each grid sub-node of the corresponding main distribution network.

[0161] In an embodiment of the present invention, after clarifying the distributed new energy consumption optimization target of each grid sub-node, the consumption collaborative scheduling optimization is performed between each grid sub-node in the main distribution network of the power system, so as to establish a collaborative scheduling model between the grid sub-nodes, define the power exchange and power balance relationship between the sub-nodes, and use a collaborative scheduling algorithm, such as a distributed algorithm or an integrated algorithm, to ensure that each sub-node achieves the optimal power allocation while meeting the consumption optimization target. Through simulation software, such as PSS / E or DIgSILENT PowerFactory, the implementation effect of collaborative scheduling is simulated, the scheduling strategy is adjusted, and the overall safety and stability of the system is ensured, thereby optimizing and generating the consumption collaborative scheduling optimization strategy of the main distribution network of the power system, guiding the actual distributed new energy consumption collaborative scheduling work, and performing corresponding execution and monitoring, and finally executing the distributed new energy consumption collaborative scheduling work between each grid sub-node of the corresponding main distribution network.

[0162] Example 5

[0163] This embodiment is different from the previous one in that it further provides a comparative experiment between the present scheme and two schemes of the prior art, illustrating that the present scheme optimizes the coordinated dispatching of each grid sub-node in the main distribution network of the power system according to the risk level of each grid sub-node under different operating conditions. This process will optimize the power distribution and consumption strategies between each grid sub-node by analyzing the risk situation of each node to ensure the efficient operation of the power system. The coordinated dispatching optimization of consumption not only considers the balance of power supply and demand, but also makes full use of the characteristics of distributed new energy to maximize its power generation potential. Through scientific scheduling algorithms, it can achieve power complementarity and resource sharing between different nodes and reduce dependence on traditional fossil energy, which not only improves the reliability and stability of power supply.

[0164] In comparative experiment 1, after comparing this solution with the first comparative file, it was found that this solution has the following effects:

[0165] D1 represents the existing technical solution: a comprehensive energy dispatch optimization method for improving the absorption capacity of distributed new energy;

[0166] D2 represents this technical solution.

[0167]

[0168]

[0169] In comparative experiment 2, this solution is compared with the second comparative file and the following effects are found:

[0170]

[0171] It should be noted that D1 represents the existing technical solution: a multi-objective optimization method for distributed renewable energy distribution network scheduling

[0172] D2 represents this technical solution.

[0173] The distributed new energy consumption dispatch optimization method for power grid based on cloud-edge fusion computing proposed by the present invention, compared with the prior art, has the beneficial effect of using distributed sensors and monitoring equipment to obtain the distributed new energy power generation data and adjustable load operation status data of each power grid sub-node on the main distribution network of the power system in real time. The acquisition of distributed new energy power generation data enables power managers to clearly understand the power generation of different distributed new energy sources, including the contribution of renewable resources such as solar energy and wind energy, so as to effectively evaluate their role in the overall energy supply. In addition, real-time monitoring of the operation status data of the adjustable system load enables the power dispatch center to flexibly respond to load fluctuations, further promoting the intelligence and efficient operation of the power system. At the same time, by configuring edge devices at each power grid sub-node and introducing edge computing nodes to perform edge data processing on the distributed new energy power generation data and the adjustable load operation status data, the corresponding distributed new energy power generation power, adjustable load demand and distributed energy storage equipment status data are obtained. This process can generate accurate power generation and load status information in real time, supporting managers to make more informed decisions in power dispatching. For example, through real-time analysis of renewable energy power generation, the dispatch center can timely adjust the operation strategy of the power grid to maximize the use of renewable energy. By monitoring the adjustable load demand and the status of energy storage equipment, it helps the power system to reasonably distribute electricity during peak hours to avoid imbalance between supply and demand. The low latency characteristics brought by edge computing enable the power system to quickly respond to external environmental changes and emergencies, providing strong support for the intelligent management of the power system. Secondly, by using the distributed renewable energy power generation, adjustable load demand and distributed energy storage equipment status data of each edge computing node to transmit to the cloud, cloud-edge fusion monitoring and management of the entire power system can be achieved. This real-time data collection capability enables the power system to quickly respond to environmental changes and load fluctuations, thereby optimizing energy dispatch. By using the cloud to design the operating conditions for the corresponding edge computing nodes, this step integrates the requirements and performance goals of each node, making the designed operating conditions more targeted and ensuring that each edge node can achieve the set goals when operating under specific conditions. By combining different operating conditions to perform constraint fusion calculations on the operating demand load for the power generation power and energy storage device status corresponding to each edge computing node, it can help identify and optimize the energy demand and supply matching of each node. This step helps to clarify the minimum and maximum operating load requirements of each node under different conditions, ensuring that each edge node can operate stably and avoid overload or underload conditions, thereby improving the flexibility and adaptability of the entire power system.By performing constraint fusion calculation of the adjustable capacity load corresponding to the power generation and operating load demand of each edge computing node under different operating conditions, it can help identify the regulation potential of each edge computing node, so as to manage the load demand more effectively. This step focuses on the adjustable capacity of each node, so that the power system can identify the load regulation potential under different operating conditions under unstable energy supply, and formulate corresponding scheduling strategies, so as to better realize the perception and regulation control capabilities of distributed new energy and adjustable system loads with low voltage access. Then, based on the distributed new energy operation demand load constraints and adjustable capacity load constraints corresponding to each edge computing node under different operating conditions, the corresponding grid sub-nodes in the main distribution network of the power system are risk assessed and analyzed. This process will help to fully understand the risk level of each grid node in different scenarios, including potential risks such as insufficient power generation capacity, load overload and energy storage equipment failure. Through quantitative evaluation, the risk level of each node can be intuitively presented, helping operation and maintenance personnel to quickly identify high-risk areas and formulate corresponding emergency plans. This risk assessment also provides an important basis for subsequent scheduling optimization, ensuring that the power system can respond in an orderly manner in the face of emergencies and reduce the risk of power outages and equipment damage. Finally, by optimizing the coordinated dispatching of each grid sub-node in the main distribution network of the power system according to the risk level of each grid sub-node under different operating conditions, this process will optimize the power distribution and consumption strategies between each grid sub-node by analyzing the risk situation of each node to ensure the efficient operation of the power system. The coordinated dispatching optimization of consumption not only takes into account the balance of power supply and demand, but also makes full use of the characteristics of distributed new energy and maximizes its power generation potential. Through scientific scheduling algorithms, it can achieve power complementarity and resource sharing between different nodes and reduce dependence on traditional fossil energy. This not only improves the reliability and stability of power supply, but also plays a positive role in promoting the widespread application of renewable energy, thereby effectively reducing the scheduling pressure faced by power system dispatchers in power system operation.

[0174] Furthermore, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described, i.e., those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention.

[0175] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but for those of ordinary skill having the benefit of this disclosure, the development effort will be a routine task of design, fabrication, and production without undue experimentation.

[0176] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed new energy consumption dispatch optimization method for power grid based on cloud-edge fusion computing, characterized by: Including steps, The real-time data of power generation and adjustable load operation status is obtained through edge devices for edge processing; The edge processed data is transmitted to the cloud for operation condition design and constraint fusion calculation to obtain the corresponding distributed new energy operation demand load constraints and adjustable capacity load constraints under different operation conditions; Based on load constraints and adjustable capacity load constraints, risk assessment and analysis are performed on the corresponding grid sub-nodes in the main distribution network of the power system to obtain the corresponding distributed renewable energy risk level of each grid sub-node under different operating conditions; According to the risk level of new energy, the coordinated dispatching of each grid sub-node in the main distribution network of the power system is optimized, and an optimization strategy for the coordinated dispatching of the main distribution network of the power system is generated to execute the coordinated dispatching of distributed new energy consumption between each grid sub-node of the corresponding main distribution network.

2. The method for optimizing the dispatching of distributed renewable energy consumption in power grids based on cloud-edge fusion computing according to claim 1, characterized in that: The steps of edge processing the real-time power generation and adjustable load operation status data through edge devices include: Through distributed sensors and monitoring equipment, real-time acquisition of distributed renewable energy power generation data and adjustable load operation status data corresponding to each grid sub-node in the main distribution network of the power system; By designing the edge device configuration for each grid sub-node in the main distribution network of the power system, a design blueprint for the edge device configuration corresponding to each grid sub-node is generated; Based on the edge device configuration design blueprint corresponding to each grid sub-node, the corresponding grid sub-nodes in the main distribution network of the power system are integrated with the edge computing node functions to obtain the corresponding edge computing nodes in the main distribution network of the power system; The corresponding edge computing nodes in the main distribution network of the power system are used to perform edge data processing on the distributed renewable energy power generation data and the adjustable load operation status data, and the distributed renewable energy power generation power, adjustable load demand and distributed energy storage equipment status data corresponding to each edge computing node are obtained.

3. The method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing according to claim 2, characterized in that: The steps of integrating the edge computing node functions of the corresponding grid sub-nodes in the main distribution network of the power system based on the edge device configuration design blueprint corresponding to each grid sub-node to obtain the corresponding edge computing nodes in the main distribution network of the power system include: Use the corresponding edge computing nodes in the main distribution network of the power system to monitor the power generation operation time of distributed renewable energy power generation data, and obtain the distributed renewable energy power generation operation time corresponding to each edge computing node; Based on the distributed renewable energy power generation operation time corresponding to each edge computing node, the distributed renewable energy power generation data corresponding to the power generation amount and environmental conditions in the operation time are calculated to obtain the distributed renewable energy power generation power corresponding to each edge computing node; Use the corresponding edge computing nodes in the main distribution network of the power system to perform edge load operation response analysis on the adjustable load operation status data to obtain the adjustable load operation response data corresponding to each edge computing node; According to the adjustable load operation response data corresponding to each edge computing node, the load operation demand inference analysis is performed on the corresponding edge computing nodes in the main distribution network of the power system to obtain the adjustable load demand corresponding to each edge computing node; Based on the distributed renewable energy power generation power and adjustable load demand corresponding to each edge computing node, the energy storage status of the energy storage devices in the grid sub-nodes corresponding to each edge computing node is evaluated and analyzed to obtain the status data of the distributed energy storage devices corresponding to each edge computing node.

4. The method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing according to any one of claims 1 to 3, characterized in that: The steps of transmitting the edge processed data to the cloud for operating condition design and constraint fusion calculation, and obtaining the corresponding distributed new energy operating demand load constraints and adjustable capacity load constraints under different operating conditions include: Use IoT technology to transmit the distributed renewable energy power generation, adjustable load demand, and distributed energy storage device status data corresponding to each edge computing node to the cloud; Use the cloud to obtain the operating function requirements and performance targets corresponding to each edge computing node, and design the operating conditions of the corresponding edge computing nodes based on the operating function requirements and performance targets corresponding to each edge computing node, so as to generate different operating condition design scenarios corresponding to each edge computing node; Based on the different operating condition design scenarios corresponding to each edge computing node, the distributed renewable energy power generation, adjustable load demand and distributed energy storage device status data corresponding to each edge computing node are divided into operating condition scenario time periods to obtain the power generation, load demand and energy storage device status of each edge computing node in the corresponding time period under different operating conditions; The operation demand load constraint fusion calculation is performed on the power generation power and energy storage device status of each edge computing node in the corresponding time period under different operating conditions, so as to obtain the distributed new energy operation demand load constraint corresponding to each edge computing node under different operating conditions; The adjustable capacity load constraint fusion calculation is performed on the power generation power and load demand of each edge computing node in the corresponding time period under different operating conditions to obtain the adjustable capacity load constraint corresponding to each edge computing node under different operating conditions.

5. The method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing according to claim 4, characterized in that: The steps of performing operation demand load constraint fusion calculation on the power generation power and energy storage device status of each edge computing node in a corresponding time period under different operating conditions to obtain the distributed new energy operation demand load constraints corresponding to each edge computing node under different operating conditions include: Obtain the environmental condition change data corresponding to each edge computing node, and perform environmental seasonal impact assessment and analysis on the corresponding power generation power based on the environmental condition change data corresponding to each edge computing node to obtain the environmental seasonal impact factor corresponding to each edge computing node; Based on the environmental seasonal impact factor corresponding to each edge computing node, the power generation power of each edge computing node in the corresponding time period under different operating conditions is updated and calculated to obtain the updated power generation power of each edge computing node in the corresponding time period under different operating conditions; Obtain the dynamic impact factor of the energy storage efficiency in the corresponding time period of each edge computing node, and perform device status update calculation on the energy storage device status of each edge computing node in the corresponding time period under different operating conditions based on the dynamic impact factor of the energy storage efficiency in the corresponding time period of each edge computing node, and obtain the updated status of the energy storage device of each edge computing node in the corresponding time period under different operating conditions; The operation demand load constraint calculation formula is used to perform a fusion calculation of the updated power generation power and the updated status of the energy storage equipment of each edge computing node in the corresponding time period under different operating conditions, so as to obtain the distributed new energy operation demand load constraints corresponding to each edge computing node under different operating conditions.

6. The method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing according to claim 5, characterized in that: The adjustable capacity load constraint fusion calculation is performed through the adjustable capacity load constraint fusion calculation formula, and the adjustable capacity load constraint fusion calculation formula is specifically: A i (t,C)=D i (t,C)-P g,i (t,C) D i (t,C)=D b,i +D s,i (t,C)+D r,i (t,C) A min,i ≤A i (t,C)≤A max,i In the formula, A i (t,C) is the adjustable capacity load constraint of the ith edge computing node in the corresponding time period t under the operating condition C, P g,i (t,C) is the power generation of the ith edge computing node in the corresponding time period t under the operating condition C, D i (t,C) is the load demand of the ith edge computing node in the corresponding time period t under the operating condition C, D b,i is the adjustable basic load corresponding to the i-th edge computing node, D s,i (t,C) is the seasonal load fluctuation of the ith edge computing node in the corresponding time period t under the operating condition C, D r,i (t,C) is the random load fluctuation of the ith edge computing node in the corresponding time period t under the operating condition C. min,i is the minimum load of the adjustable capacity corresponding to the i-th edge computing node, A max,i is the maximum load of the adjustable capacity corresponding to the i-th edge computing node.

7. The method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing according to claim 5 or 6, characterized in that: The risk assessment analysis is calculated by a distributed new energy risk level calculation formula, and the distributed new energy risk level calculation formula is specifically: In the formula, R i (t,C) is the distributed new energy risk level of the ith edge computing node in the corresponding time period t under the operating condition C, L i (t, C) is the distributed renewable energy operation demand load constraint of the ith edge computing node in the corresponding time period t under the operating condition C, U(C) is the external uncertainty factor under the operating condition C, β is the uncertainty risk impact weight coefficient, and A i (t, C) is the adjustable capacity load constraint of the i-th edge computing node in the corresponding time period t under the operating condition C, and ε is a non-zero adjustment constant.

8. The method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing according to claim 7, characterized in that: According to the risk level of new energy, the coordinated dispatching optimization of the consumption between each grid sub-node in the main distribution network of the power system is performed, and the coordinated dispatching optimization strategy of the main distribution network of the power system is generated to perform the coordinated dispatching of distributed new energy consumption between each grid sub-node of the corresponding main distribution network. The steps include: According to the distributed renewable energy risk level of each grid sub-node under different operating conditions, the consumption and dispatching demand of each grid sub-node in the main distribution network of the power system is predicted and analyzed, and the distributed renewable energy consumption and dispatching demand of each grid sub-node under different operating conditions is obtained; Based on the distributed renewable energy consumption dispatching requirements of each grid sub-node under different operating conditions, the corresponding grid sub-nodes in the main distribution network of the power system are analyzed for consumption optimization targets, so as to obtain the distributed renewable energy consumption optimization targets corresponding to each grid sub-node under different operating conditions; Based on the distributed new energy consumption optimization targets corresponding to each grid sub-node under different operating conditions, the consumption coordinated dispatch optimization is carried out between each grid sub-node in the main distribution network of the power system, and the power system main distribution network consumption coordinated dispatch optimization strategy is generated to execute the distributed new energy consumption coordinated dispatch work between each grid sub-node of the corresponding main distribution network.

9. The method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing according to claim 8, characterized in that: The updated power generation formula is: P i (t,C)=P g,i (t,C)*η g,i (t,C)*S i (t); P min,i ≤P i (t,C)≤P max,i ; Among them, P i (t,C) is the updated power generation of the ith edge computing node in the corresponding time period t under the operating condition C, P g,i (t,C) is the power generation of the ith edge computing node in the corresponding time period t under the operating condition C, η g,i (t,C) is the power generation efficiency of the ith edge computing node in the corresponding time period t under the operating condition C, S i (t) is the environmental seasonal impact factor of the i-th edge computing node in the corresponding time period t, P min,i is the minimum power generation corresponding to the i-th edge computing node, P max,i is the maximum power generation corresponding to the i-th edge computing node.

10. The method for optimizing the dispatching of distributed renewable energy consumption in power grid based on cloud-edge fusion computing according to claim 9, characterized in that: The specific calculation formula of the operation demand load constraint is: L i (t,C)=E i (t,C)-(P i (t,C)+P d,i (t,C)); Where, L i (t,C) is the load constraint of distributed renewable energy operation demand of the ith edge computing node under the operating condition C in the corresponding time period t, E i (t,C) is the updated state of the energy storage device of the ith edge computing node in the corresponding time period t under the operating condition C, P i (t,C) is the updated power generation of the ith edge computing node in the corresponding time period t under the operating condition C, P d,i (t,C) is the discharge power of the energy storage device of the i-th edge computing node in the corresponding time period t under operating condition C.

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