Energy scheduling method, system and device of distributed energy system

Abstract

This application relates to an energy dispatching method, system, and apparatus for a distributed energy system. The method includes: determining the operating status of each energy device in the distributed energy system based on the operating data of the energy devices after synchronization of each distributed node; determining an energy dispatching strategy that minimizes a preset estimation function based on the operating status of each energy device and a pre-constructed energy device graph model, with the objective of minimizing the preset estimation function. The estimation function is used to determine at least one of energy loss and communication delay during the energy dispatching process; and dispatching energy in the distributed energy system based on the energy dispatching strategy. The energy device graph model includes multiple nodes and edges connecting each node; nodes represent energy devices, and edges represent the connection relationships between the various energy devices in the distributed energy system. This method can improve the accuracy of energy dispatching.

Description

Technical Field

[0001] This application relates to the field of energy dispatching technology, and in particular to an energy dispatching method, system, device, computer equipment, storage medium and computer program product for a distributed energy system. Background Technology

[0002] With the development of Internet of Things (IoT) technology, distributed energy systems are increasingly being used in smart grids, home energy management systems, and other fields. Various energy devices are interconnected to form a distributed energy network, which together completes the tasks of power storage, distribution, and scheduling.

[0003] In traditional solutions, if a distributed energy system needs to be scheduled, it is mostly necessary to allocate resources and plan scheduling paths for each energy device in the distributed energy system based on fixed rules and preset processes, and formulate an energy scheduling strategy.

[0004] However, traditional strategy formulation methods often lack the ability to perceive and optimize dynamic environments, such as changes in distributed network topology and load fluctuations of energy equipment. Furthermore, when facing large-scale distributed networks, the scheduling complexity increases significantly, meaning that traditional solutions suffer from low accuracy in energy scheduling. Summary of the Invention

[0005] Therefore, it is necessary to provide an energy dispatching method, apparatus, computer equipment, computer-readable storage medium, and computer program product for a distributed energy system that can improve the accuracy of energy dispatching, in order to address the aforementioned technical problems.

[0006] Firstly, this application provides an energy dispatching method for a distributed energy system. The method includes:

[0007] Based on the operating data of energy devices after synchronization of each distributed node in the distributed energy system, the operating status of each energy device in the distributed energy system is determined.

[0008] Based on the operating status of each energy device and a pre-built energy device graph model, with the goal of minimizing a preset estimation function, an energy scheduling strategy is determined that minimizes the preset estimation function. The estimation function is used to determine at least one of energy loss and communication delay during the energy scheduling process.

[0009] Based on the energy dispatch strategy, the energy of the distributed energy system is dispatched;

[0010] The energy device graph model includes multiple nodes and edges connecting each node. Nodes represent energy devices, and the edges of the energy device graph model are used to represent the connection relationships between various energy devices in the distributed energy system.

[0011] Secondly, this application also provides an energy dispatching system for a distributed energy system. The system includes servers and multiple distributed nodes that are interconnected.

[0012] The distributed node is used to broadcast the collected operating data of energy equipment in the distributed energy system, and to synchronize the operating data with other distributed nodes besides itself, and upload the synchronized operating data to the server.

[0013] The server is used to process the synchronized operating data using any of the above-mentioned distributed energy system energy scheduling methods, determine the energy scheduling strategy, and schedule the energy of the distributed energy system based on the energy scheduling strategy.

[0014] Thirdly, this application also provides an energy dispatching device for a distributed energy system. The device includes:

[0015] The data acquisition module is used to determine the operating status of each energy device in the distributed energy system based on the operating data of the energy devices after synchronization of each distributed node in the distributed energy system.

[0016] The strategy formulation module is used to determine an energy dispatch strategy that minimizes a preset estimation function based on the operating status of each energy device and a pre-built energy device graph model. The estimation function is used to determine at least one of energy loss and communication delay in the energy dispatch process.

[0017] The system scheduling module is used to schedule the energy of the distributed energy system based on the energy scheduling strategy.

[0018] The energy device graph model includes multiple nodes and edges connecting each node. Nodes represent energy devices, and the edges of the energy device graph model are used to represent the connection relationships between various energy devices in the distributed energy system.

[0019] Fourthly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above-described embodiments of the energy dispatching method for a distributed energy system.

[0020] Fifthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps in the above-described embodiments of the energy dispatching method for a distributed energy system.

[0021] Sixthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps in the above-described embodiments of the energy dispatching method for a distributed energy system.

[0022] The energy dispatching method, apparatus, computer equipment, storage medium, and computer program products for the aforementioned distributed energy system differ from traditional solutions. This application determines the operating status of each energy device in the distributed energy system based on the operating data of the energy devices after synchronization of each distributed node. This operating status not only reflects the overall operating condition of the distributed energy system but also provides an accurate data foundation for subsequent energy dispatching strategy formulation. Then, based on the operating status of each energy device and a pre-constructed energy device graph model, an energy dispatching strategy is determined with the objective of minimizing a preset estimation function. The pre-constructed energy device graph model abstracts energy devices in the distributed energy system as nodes and the connections between devices as edges. This graph model approach effectively abstracts and simplifies complex distributed networks, reducing the difficulty of analyzing and processing large-scale distributed networks. Furthermore, the estimation function considers key factors such as energy loss and / or communication latency. By determining the energy dispatching strategy that minimizes the preset estimation function, a more demand-responsive, resource-efficient, and latency-reducing energy dispatching strategy can be found in large-scale distributed energy systems, thereby improving the accuracy of energy dispatching. Attached Figure Description

[0023] Figure 1 This is an application environment diagram of an energy dispatching method for a distributed energy system in one embodiment.

[0024] Figure 2 This is a flowchart illustrating an energy dispatching method for a distributed energy system in one embodiment.

[0025] Figure 3 This is a flowchart illustrating the steps involved in developing an energy dispatch strategy in one embodiment.

[0026] Figure 4 This is a flowchart illustrating the energy dispatching method for a distributed energy system in another embodiment;

[0027] Figure 5 This is a detailed flowchart illustrating an energy dispatching method for a distributed energy system in one embodiment.

[0028] Figure 6 This is a structural block diagram of the energy dispatch system of a distributed energy system in one embodiment;

[0029] Figure 7 This is a structural block diagram of an energy dispatching device in a distributed energy system according to one embodiment;

[0030] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description, in conjunction with the accompanying drawings and embodiments, will further illustrate the content of this application. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] The energy dispatching method for distributed energy systems provided in this application can be applied to, for example... Figure 1 In the application environment shown, distributed node 102 communicates with server 104 via a network. The data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on other network servers.

[0033] Specifically, distributed nodes 102 can each collect operational data from energy devices and store it locally. They then share this collected data with other distributed nodes 102 via broadcast and synchronize the data. Finally, the synchronized operational data is uploaded to server 104. Server 104, based on the synchronized operational data from each distributed node in the distributed energy system, determines the operational status of each energy device. Then, based on the operational status of each energy device and a pre-built energy device graph model, it determines an energy scheduling strategy that minimizes a preset evaluation function. Finally, server 104 schedules the energy in the distributed energy system based on this energy scheduling strategy. It should be noted that the energy device graph model includes multiple nodes and edges connecting them. Nodes represent energy devices, edges represent the connections between energy devices in the distributed energy system, and the evaluation function is used to determine at least one of energy loss and communication latency during the energy scheduling process.

[0034] The distributed node 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0035] In one embodiment, such as Figure 2 As shown, an energy dispatching method for a distributed energy system is provided, which can be applied to... Figure 1Taking server 104 as an example, the following steps are included:

[0036] S100 determines the operating status of each energy device in the distributed energy system based on the operating data of the energy devices after synchronization of each distributed node in the distributed energy system.

[0037] A distributed energy system refers to a system formed by interconnecting multiple dispersed energy devices through a network. These energy devices include, but are not limited to, solar panels, wind turbines, and energy storage devices, and can collectively perform tasks such as power storage, distribution, and scheduling. Distributed nodes refer to different computing nodes distributed within the distributed energy system, which can perform data collection, status analysis, and operation scheduling of energy devices within a certain area.

[0038] For example, in a distributed energy system, energy devices such as solar panels, wind turbines, and energy storage batteries generate a large amount of operational data. Due to the characteristics of distributed systems, the data collection time and frequency of each distributed node may differ, leading to inconsistencies in the operational data collected by each distributed node. Therefore, data synchronization is necessary before data processing. This can be achieved by each distributed node using its own consensus and broadcast mechanisms to synchronize data with other distributed nodes. When the operational data collected by a distributed node is updated, it can broadcast the update to the entire network (or neighboring nodes) incrementally or in full. After receiving the updated operational data, other distributed nodes automatically synchronize their data based on the consensus mechanism of the distributed energy system to achieve network-wide data consistency.

[0039] Furthermore, while the data synchronization process among distributed nodes can be performed without the involvement of a central server, the subsequent formulation of energy dispatch strategies can be completed by servers or server groups. For example, when there are gateways, edge servers, or cloud nodes with strong computing capabilities in the distributed energy system, these servers can collect the operational data after data synchronization to determine the operating status of each energy device in the distributed energy system. This operating status includes, but is not limited to, the charging and discharging status of energy storage devices and the power generation status of power generation devices. Then, an energy dispatch strategy is formulated and broadcast as dispatch instructions to each energy device for implementation. In addition, if the computing resources of a single server device in the distributed energy system are insufficient, the task of formulating energy dispatch strategies can be distributed to multiple execution nodes. Each execution node shares the data analysis process and processing results with other execution nodes besides itself, ultimately generating a feasible energy dispatch strategy.

[0040] S200, based on the operating status of each energy device and the pre-built energy device diagram model, determines the energy dispatch strategy that minimizes the preset estimation function.

[0041] The energy device graph model is an abstract representation of the entire distributed energy system structure. It includes multiple nodes and edges connecting them. Nodes represent energy devices, and edges represent the connections between these devices. Based on this model, the transmission paths and flows of energy between devices can be clearly and intuitively presented. The evaluation function is used to determine at least one of energy loss and communication delay during energy dispatching. Different energy dispatching strategies may result in varying line losses and time delays in information exchange between energy devices. Lower energy losses and lower communication delays indicate a better energy dispatching strategy; therefore, the evaluation function can be used to evaluate the merits of different energy dispatching strategies.

[0042] For example, based on the operating status of energy equipment and a pre-built energy equipment graph model, several candidate energy dispatch strategies can be initially planned. For instance, when solar panels generate excess power, one option is to transfer the excess power to nearby energy storage batteries for storage; another option is to directly supply power to nearby equipment with high power demand. Then, for each candidate energy dispatch strategy, relevant parameters are substituted into a preset estimation function for calculation. The energy loss, communication latency, etc., of each candidate energy dispatch strategy are calculated, and the candidate energy dispatch strategy that minimizes the estimation function is selected as the energy dispatch strategy.

[0043] It should be noted that some optimization algorithms can be used in the process of formulating the energy dispatch strategy to accelerate the generation of the strategy and improve the accuracy of the energy dispatch strategy, such as genetic algorithms, particle swarm optimization, and heuristic algorithms.

[0044] S300 is an energy dispatching system that dispatches energy from distributed energy systems based on energy dispatching strategies.

[0045] Among them, the energy dispatch strategy includes the power generation of each power generation device in the distributed energy system, the charging and discharging period and charging and discharging power of the energy storage device, the power consumption period of the power consumption device, and the energy flow path in the system.

[0046] Following the steps outlined above, after determining the energy dispatch strategy, the server can break down the strategy into energy dispatch instructions and send these instructions to various energy devices in the distributed energy system. For example, it can send energy dispatch instructions to be executed to power generation equipment, energy storage equipment, and power consumption equipment to adjust power generation, charging / discharging power, and power consumption accordingly. Furthermore, energy dispatch instructions can adjust the energy transmission paths within the distributed energy system. For instance, through transmission lines and distribution equipment, energy can be transferred from power generation equipment or energy storage equipment to power consumption equipment or other energy storage equipment. During this process, energy should flow according to the energy flow paths defined in the energy dispatch strategy.

[0047] The energy dispatching method for the aforementioned distributed energy system differs from traditional solutions. This application determines the operating status of each energy device in the distributed energy system based on the synchronized operating data of the distributed nodes. This operating status not only reflects the overall operating condition of the distributed energy system but also provides an accurate data foundation for subsequent energy dispatching strategies. Then, based on the operating status of each energy device and a pre-constructed energy device graph model, an energy dispatching strategy is determined with the objective of minimizing a preset estimation function. The pre-constructed energy device graph model abstracts energy devices in the distributed energy system as nodes and the connections between devices as edges. This graph model approach effectively abstracts and simplifies complex distributed networks, reducing the difficulty of analyzing and processing large-scale distributed networks. Furthermore, the estimation function considers key factors such as energy loss and / or communication latency. By determining the energy dispatching strategy that minimizes the preset estimation function, a more demand-responsive, resource-efficient, and latency-efficient energy dispatching strategy can be found in large-scale distributed energy systems, thereby improving the accuracy of energy dispatching.

[0048] In one embodiment, such as Figure 3 As shown, S200 includes:

[0049] S210, take the starting node in the energy equipment diagram model as the current node.

[0050] S220: With the goal of minimizing the preset evaluation function, find the target node in the next level node of the current node that minimizes the preset evaluation function, and determine the target node as the new current node.

[0051] S230: Determine if the current node is the end node. If not, return to S220 until the end node is found, and obtain the energy scheduling path of the distributed energy system.

[0052] S240 determines the energy dispatch strategy for the distributed energy system based on the energy dispatch path.

[0053] In this energy device graph model, nodes represent energy devices (such as photovoltaic inverters, battery packs, and smart loads), and edges represent reachable communication or energy transmission routes between these devices. Edges can be directed or undirected. If the nodes at both ends of an edge have bidirectional reachability, the edge can be undirected; if the nodes at both ends of an edge have a specific directionality, allowing only unidirectional communication or energy transmission, the edge is directed. Furthermore, in this embodiment, if the preset evaluation function is a multi-objective function representing energy loss, communication latency, bandwidth usage cost, etc., the edge weights represent the importance of each objective. For example, the edge weights connecting node A and node B can be 0.1, 0.4, and 0.5, indicating that to measure the quality of energy transmission from node A to node B, energy loss accounts for 0.1, communication latency accounts for 0.4, and bandwidth usage cost accounts for 0.5. The quality of energy transmission from node A to node B can then be evaluated using a weighted calculation method. The edge weights corresponding to edges connecting different nodes can be different. In a distributed energy system, an energy dispatch path refers to the route that energy travels from a starting point (e.g., a power generation device) to a destination (e.g., a power consumption device), consisting of a series of nodes (energy devices) and connecting edges (connections between energy devices).

[0054] For example, the valuation function value Represents the known minimum cost from the start node to the current node. and the heuristic function value from the current node to the end node. The sum. Heuristic function value. This refers to the "ideal" or "estimated" cost from the current node to the end node. Heuristic function values ​​can guide the search process to converge faster and determine the energy scheduling path more quickly. It should be noted that determining the heuristic function value... It is necessary to follow the monotonicity principle and the acceptability principle. The monotonicity principle means that for any two nodes n and m in the energy equipment graph model, the cost from node n to the end node should not be greater than the sum of the cost from node n to node m and the cost from node m to the end node, which is expressed by formula (1) as follows:

[0055] (1)

[0056] The acceptability principle requires that for any node n, the heuristic function value It cannot be greater than the actual shortest cost from node n to the end node, expressed by formula (2):

[0057] (2)

[0058] For example, heuristic function values It can be defined as the minimum energy loss and remaining power between nodes, expressed by formula (3):

[0059] (3)

[0060] In equation (3), This is represented by the remaining electricity of the energy device represented by node n. Characterizes the energy loss along the most ideal path. and As weight.

[0061] If the distributed energy system is more concerned with communication delay, then the heuristic function value can be represented as the communication delay information on the node or edge, expressed by formula (4):

[0062] (4)

[0063] If a distributed energy system has multiple optimization objectives (such as simultaneously optimizing communication latency and energy loss), the heuristic function value can be determined by weighting the multiple objectives. .

[0064] Specifically, before searching for energy dispatch paths, two sets can be maintained in advance: a priority set (Open List) and a visited node set (Closed List). The priority set stores nodes waiting to be searched, while the visited node set stores nodes that have already been searched. Start Node It can be a photovoltaic inverter, end node It could be a battery pack. First, initialize all nodes n in the energy device graph model, setting the nodes... In the insertion preference set, let Other nodes n This is because the actual minimum cost from the starting node to other nodes is unknown, let This indicates this initial, unknown state.

[0065] In the initial state, the starting node in the energy equipment graph model is taken as the current node. First, it is determined whether the current node is the ending node. If it is, the search ends directly. If not, the search for the next level node of the current node begins.

[0066] If the next level node of the current node does not exist, the current node is placed into the set of visited nodes. This means that there are no new paths for the current node to continue exploring. Placing it into the set of visited nodes means that the node has been fully explored and does not need to be processed again, thus improving exploration efficiency.

[0067] If the next-level node x of the current node n exists, then check if the next-level node x exists in the priority set. If the next-level node x exists in the priority set, it means that node x has not been fully explored, and the evaluation function value of the current node n needs to be further calculated. ( The estimated function value of the next level node x. ( If the value of the evaluation function of the next level node x is... The estimated function value is less than the current node n. If so, then node x is taken as the next node of the current node, i.e., the target node, and the minimum valuation function value of the nodes in the priority set is updated.

[0068] If the next-level node x of the current node n does not exist in the priority set, then check if it exists in the visited node set. If the next-level node x exists in the visited node set, it means that node x has been explored before. However, it has been found that the path from the current node n to the next-level node x may be better than the previous exploration path. At this time, node x can be removed from the visited node set and put back into the priority set to re-evaluate node x and its subsequent paths.

[0069] If the next-level node x of the current node n does not exist in the priority set or the visited node set, it means that the next-level node x may be a node that has not been discovered in previous explorations. In this case, node x is added to the priority set, indicating that node x still needs to be explored, and the evaluation function value of node x is calculated. .

[0070] The above operations can effectively reduce the exploration of duplicate nodes, and if the value of the evaluation function of the next-level node x is... The estimated function value is less than the current node n. If a node x is selected as the next node after the current node, i.e., the target node, then the path from the current node to the target node is less costly (resulting in a smaller evaluation function value) compared to going directly from the current node to the end node, leading to a better energy dispatching path. This exploration is repeated until the end node in the energy equipment graph model is found, thus completing the path exploration and obtaining the energy dispatching path that minimizes the preset evaluation function.

[0071] The aforementioned energy dispatch path belongs to the energy dispatch strategy. The energy dispatch path that minimizes the preset valuation function can enable energy to be allocated more rationally and efficiently in the distributed energy system, such as minimizing energy loss and communication costs during energy transportation.

[0072] In this embodiment, by using an energy equipment graph model and a minimization estimation function, the optimal energy dispatch path can be selected from among many possible energy dispatch paths. Furthermore, by combining a heuristic function, the path search process can be guided by a clear objective, thereby quickly finding a suitable energy dispatch path, improving energy dispatch efficiency, and reducing energy loss and communication costs during energy transmission.

[0073] In one embodiment, the runtime data includes runtime data of various data structure types, and the data synchronization strategy is different for each type of runtime data.

[0074] For example, in a distributed energy system, the data structure type needs to be defined before data synchronization. For instance, the operating data can be divided into different CRDTs (Conflict-free Replicated Data Types). For example, the operating data of energy devices can be divided into numerical types, set types, and mapping types.

[0075] Specifically, numerical operational data can be operational data that can be represented by count values, such as the number of charge / discharge cycles of energy storage devices, the operating time of energy devices, the power generation of generators, and the amount of data transmitted. Numerical operational data can be further divided into operational data that only supports accumulation and operational data that supports both accumulation and rollback. For example, the number of battery charge cycles and operating time of energy storage devices, as well as the power generation of generators, are all operational data that only support accumulation; the number of battery charge cycles and operating time can only be accumulated.

[0076] Set-type operational data can be a set of task lists for energy devices, a set of energy device lists, a set of online energy devices, a set of faulty energy devices, a set of scheduling strategies, a set of event records, etc. For example, in a distributed energy system, there may be multiple tasks that need to be assigned to different energy devices or distributed nodes for processing. For instance, each charging station may need to perform tasks such as charging, discharging, status detection, and fault diagnosis. These tasks can be represented as a set of task lists.

[0077] Map-type runtime data can be represented using key-value pairs. This refers to the various data involved in data processing and operations using the Map-CRDT data structure, and can be represented as follows: Each key value Treat it as an independent data type. If concurrent updates of data are required, then for each key-value pair... Maintain the corresponding data synchronization strategy, and execute the corresponding data synchronization operation key by key during data synchronization, so that different key values... The data synchronization is independent of each other and can maintain consistency.

[0078] In this embodiment, the running data is divided into different data structure types, and different data synchronization strategies are set for different types of running data. This can alleviate the technical defects of low efficiency and low accuracy caused by using a general data synchronization strategy, thereby achieving data consistency of distributed nodes efficiently and accurately.

[0079] In one embodiment, the data structure type includes numeric type, set type, and mapping type. When the data structure type of the running data is numeric type, the data synchronization strategy of the running data is the maximum value strategy for distributed nodes. When the data structure type of the running data is set type, the data synchronization strategy of the running data is the priority addition strategy or the priority deletion strategy. When the data structure type of the running data is mapping type, the data synchronization strategy of the running data is the key-by-key synchronization strategy.

[0080] Following the above embodiments, when the structure type of the running data is numerical, the data synchronization strategy for the running data is the per-distributed-node maximum value strategy. The per-distributed-node maximum value strategy means that when there are concurrent updates on multiple distributed nodes, these distributed nodes use a counter or timestamp to maintain their local state. During data synchronization, in order to ensure that the synchronized value does not lose information, the maximum value of the counter on each distributed node in various dimensions (such as timestamp, version number, count value, etc.) will be taken. For example, the numerical structure-type runtime data collected by each distributed node can be called a local count value. The local count value can record timestamps, version numbers, count values, etc. For example, when distributed node A and distributed node B perform incremental operations on the same counter during a certain runtime data update, such as the counter recording the number of times a certain battery is charged, the local count value of distributed node A is (2, 3) and the local count value of distributed node B is (1, 4), representing two dimensions respectively. The data synchronization strategy of taking the maximum value of each coordinate will compare the counter value of each distributed node for each dimension and take the maximum value of that dimension as the counter value after data synchronization. For example, for the first dimension, the maximum value in distributed nodes A and B is 2, and for the second dimension, the maximum value in distributed nodes A and B is 4. Therefore, after data synchronization, the count value of the counter in distributed nodes A and B is (2, 4).

[0081] Specifically, when numerical operational data only supports accumulation, if there are N distributed nodes in the distributed energy system, the numerical operational data collected by each distributed node is denoted as follows: The count value under a globally consistent view can then be represented as:

[0082] (5)

[0083] By employing a strategy of taking the maximum value from each distributed node, the synchronized running data is as follows:

[0084] (6)

[0085] In equation (6), This is the counter value after data synchronization. The counter value collected by the i-th distributed node. The counter value is collected by other distributed nodes besides itself. Formula (6) is applicable to those operating data that only support accumulation, such as battery charging times, equipment running time, power generation or energy storage, equipment data upload volume, battery discharge times, etc.

[0086] When the data structure is numeric, there is also a type of data that supports both accumulation and decrement. For this type of data, it can be treated as two independent counters that only support accumulation. The data synchronization strategy remains the same: take the maximum value for each distributed node. The count value under a globally consistent view can be represented as:

[0087] (7)

[0088] Accordingly, the data obtained after synchronizing by taking the maximum value per distributed node can be represented as:

[0089] (8)

[0090] When the data structure type of the running data is a set, the data synchronization strategy adopted is either a priority addition strategy or a priority deletion strategy. In a distributed energy system, the running data of the set type can include, but is not limited to, task list sets, device list sets, etc. Elements in a set typically have a unique label to identify them. During system operation, tasks and devices are added or removed. Adding and deleting elements in the corresponding sets can be performed without conflict. For example, for a task list set, set A represents the observed addition set (where elements are tasks newly added to the task list set), and set B represents the observed deletion set (where elements are tasks deleted from the task list set). Then, in the entire distributed energy system, the total number of added sets is... Total Deletion Set It can be represented as:

[0091] (9)

[0092] (10)

[0093] For example, the set of additions observed by distributed node A. ={device1, device2}, the observed deletion set ={Device 2}, the set of additions observed by distributed node B. ={device2, device3}, the observed deletion set ={device 1}, then in the entire distributed energy system, and merge, and Merge, add total collection ={Device 1, Device 2, Device 3}. Total deletion set. ={device1, device2}.

[0094] Furthermore, when multiple distributed nodes update concurrently, the same element may be marked as "added" and "deleted" by different distributed nodes, causing data synchronization conflicts. Priority add and priority delete strategies are used to resolve these conflicts. If a priority add strategy is used, it means that add operations have higher priority than delete operations. That is, if an element is marked as both "added" and "deleted," it will be retained in the synchronized set. If a priority delete strategy is used, it means that delete operations have higher priority than add operations. That is, if an element is marked as both "added" and "deleted," it will be deleted from the synchronized set. For example, for a set of online devices, suppose distributed node A adds device 1 to the set, and distributed node B considers device 1 offline and removes it. If a priority add strategy is used, device 1 should be included in the synchronized set of online devices. If a priority delete strategy is used, device 1 will not be included. The principles and methods for data synchronization of task list sets, faulty device sets, etc., are similar. In addition, the charging status of batteries or other energy storage devices in a distributed energy storage system can also be added and deleted. For example, if distributed node A records that a certain battery has started charging, and another distributed node B records that the battery has finished charging, if a priority deletion strategy is adopted, the set of charging devices after data synchronization will not include the battery. If a priority addition strategy is adopted, the set of charging devices after data synchronization should include the charging battery.

[0095] When the data structure type of the operational data is a mapping type, the data synchronization strategy for the operational data is a key-by-key synchronization strategy. Mapping type data can be stored as key-value pairs. For example, an energy device identifier and its operating parameters can form a key-value pair, with the energy device identifier as the key and its operating parameters (such as whether it is charging) as the value. In a distributed energy system, there are many mapping type operational data, and the key-value pairs of different operational data are also different. Therefore, each operational data can be treated as an independent key-value pair, and independent operations can be performed on each key-value pair to achieve independent and eventual consistency in the updates of different keys. When performing data synchronization operations on each key-value pair, the data synchronization strategy adopted can be the aforementioned strategy of taking the maximum value per distributed node, priority addition strategy, priority deletion strategy, and priority last write strategy (i.e., determining the synchronized operational data based on timestamps and version numbers, with the last write taking precedence), etc.

[0096] It's important to note that each distributed node maintains a vector clock for its collected runtime data when broadcasting it. Simultaneously, the runtime data received from other distributed nodes also carries a vector clock. Before data synchronization, it's necessary to determine the update time of the runtime data based on the vector clock and to ascertain whether the updates are sequential or parallel. If the updates are identified as sequential, data synchronization can be performed sequentially to reduce the risk of logical errors. If the updates are identified as concurrent, these parallel updates may involve different parts of the same data, leading to data conflicts during synchronization. Therefore, in this case, data synchronization strategies such as taking the maximum value from each distributed node, prioritizing additions, prioritizing deletions, or prioritizing last writes need to be employed based on the data structure type.

[0097] In this embodiment, appropriate data synchronization strategies are set for different data structure types of runtime data. For numerical runtime data, a strategy of taking the maximum value per distributed node is adopted, which can effectively realize the data synchronization of numerical data. For set runtime data, a priority addition strategy or priority deletion strategy is adopted, which can flexibly control the set elements after data synchronization and reduce the risk of data loss or data duplication. For mapping runtime data, a key-by-key synchronization strategy is adopted, which can ensure that each key-value pair maintains consistency, improve data synchronization efficiency, and significantly reduce the complexity and communication overhead of traditional lock or transaction mechanisms in high-concurrency scenarios.

[0098] In one embodiment, such as Figure 4 As shown, after S200, the method further includes:

[0099] S410: Periodically update the operating status of each energy device in the distributed energy system, or update the operating status of each energy device in the distributed energy system when the fluctuation range of the operating status of each energy device exceeds the preset range threshold.

[0100] S420 updates the energy dispatch strategy of the distributed energy system based on the updated operating status of each energy device and the pre-built energy device graph model.

[0101] Following the above embodiments, since the operating state of the distributed energy system is constantly changing, the operating state of each energy device also changes accordingly. However, updating the energy dispatch strategy in real time to reflect changes in the operating state of the energy devices may incur unnecessary computational overhead. Therefore, in this embodiment, the energy dispatch strategy can be updated periodically or only reformulated when the operating state of the energy devices fluctuates significantly.

[0102] For example, a fixed time interval can be set, such as every hour, to synchronize data between distributed nodes. Based on the synchronized operational data, the operating status of each energy device in the distributed energy system is updated. Besides periodic updates, an update is also triggered when the fluctuation range of the energy device's operating status exceeds a preset threshold. For instance, if the power generation of the distributed energy system's generating equipment fluctuates significantly within a short period, exceeding the preset threshold, the operating status of the generating equipment needs to be updated immediately. Furthermore, the updated operating status more accurately reflects the actual situation of the distributed energy system. Therefore, the energy dispatch strategy of the distributed energy system can be updated in the same way, based on the updated operating status of each energy device and a pre-built energy device graph model. It should be noted that if the energy dispatch strategy formulated based on the updated operating status and the pre-built energy device graph model is less effective than the previous energy dispatch strategy, the previous energy dispatch strategy can continue to be used; otherwise, the updated energy dispatch strategy will be adopted.

[0103] In this embodiment, by updating periodically or triggering updates when the operating status fluctuates significantly, the latest operating status of energy equipment can be obtained in a timely manner, and the energy dispatch strategy can be adjusted accordingly. This allows the energy dispatch strategy to be adapted as closely as possible to the actual distributed energy system, thereby improving the accuracy and reliability of the energy dispatch strategy.

[0104] To provide a clearer explanation of the energy dispatching method for the distributed energy system provided in this application, the following is in conjunction with the appendix. Figure 5 and one A detailed embodiment will be explained, which includes the following steps:

[0105] S501 determines the operating status of each energy device in the distributed energy system based on the operating data of the energy devices after synchronization of each distributed node in the distributed energy system.

[0106] S502, take the starting node in the energy equipment diagram model as the current node.

[0107] S503, with the goal of minimizing the preset evaluation function, find the target node in the next level node of the current node that makes the preset evaluation function reach its minimum value, and determine the target node as the new current node.

[0108] S504: Determine if the current node is the end node. If not, return to S503 until the end node is found, and obtain the energy scheduling path of the distributed energy system.

[0109] S505 determines the energy dispatch strategy of the distributed energy system based on the energy dispatch path, and dispatches the energy of the distributed energy system based on the energy dispatch strategy.

[0110] S506: When the fluctuation range of the operating status of each energy device exceeds the preset amplitude threshold, update the operating status of each energy device in the distributed energy system.

[0111] S507 updates the energy dispatch strategy of the distributed energy system based on the updated operating status of each energy device and the pre-built energy device diagram model.

[0112] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0113] In one embodiment, this application also provides an energy dispatching system 600 for a distributed energy system, including multiple distributed nodes 102 and a server 104 that are interconnected, i.e., there are communication connections between each distributed node 102 and between each distributed node 102 and the server 104.

[0114] Distributed node 102 is used to broadcast the collected operating data of energy equipment in the distributed energy system, and to synchronize the operating data with other distributed nodes 102 except itself, and upload the synchronized operating data to server 104.

[0115] Server 104 is used to receive and determine the operating status of each energy device in the distributed energy system based on the operating data of the energy devices synchronized by each distributed node 102 in the distributed energy system. Based on the operating status of each energy device and a pre-built energy device graph model, with the goal of minimizing a preset estimation function, an energy scheduling strategy is determined that minimizes the preset estimation function. The estimation function is used to determine at least one of energy loss and communication delay in the energy scheduling process. Based on the energy scheduling strategy, the energy of the distributed energy system is scheduled. The energy device graph model includes multiple nodes and edges connecting each node. Nodes represent energy devices, and edges represent the connection relationships between each energy device in the distributed energy system.

[0116] It is understood that the server 104 in this embodiment can be a cloud server, and the solution provided by the system is similar to the solution described in the above method. Therefore, the specific limitations of the energy dispatch system embodiment of the distributed energy system provided in this embodiment can be found in the limitations of the energy dispatch method of the distributed energy system above, and will not be repeated here.

[0117] For example, distributed nodes can be deployed in various energy devices within a distributed energy system. Each energy device integrates a lightweight embedded operating environment, meaning the distributed node includes an mDNS (Multicast Domain Name System) service module, an HTTP Server module, and a data status management module. The mDNS service module is used to automatically identify other energy devices in a local area network (LAN) or P2P-based network environment, i.e., to automatically identify other distributed nodes. It then registers its own service information, including but not limited to the energy device's unique device identifier, a list of accessible API interfaces, and network connection parameters, through periodic broadcasting. Through the mDNS mechanism, newly connected energy devices can automatically broadcast their services in the LAN or P2P network, achieving rapid mutual recognition among energy devices. Other online energy devices can also update their topology information in real time and incorporate new energy devices into the network.

[0118] The HTTP service module provides a unified RESTful interface to the external devices, supporting the reading and writing of operational data (such as obtaining the power generation of the generator and setting the operating mode of the energy equipment), issuing control commands (such as opening / closing the charging and discharging circuit), and forwarding and processing of synchronization messages. The HTTP service module allows each energy device to synchronize data and issue control commands based on the RESTful style HTTP request / response pattern, and combines it with lightweight JSON serialization format for data encoding and decoding, reducing encoding overhead and facilitating fast message parsing in embedded hardware environments, thereby improving message transmission efficiency.

[0119] The data status management module maintains local operating data and copies of operating data from surrounding energy devices. Based on the Conflict-Free Replicated Data Type (CRDT) structure, it employs appropriate data synchronization strategies to synchronize the operating data of energy devices. By maintaining local operating data and copies of operating data from surrounding energy devices, even when energy devices are disconnected from the main network or can only communicate with local subnets (e.g., when the network bandwidth of the distributed energy system is limited or the connection is unstable (or even network partitions occur), local synchronization between energy devices can still achieve local data consistency. Within a local area, all operating data updates are marked with local clock or version information and stored locally / in the subnet. After the network recovers, the data status management module can continue to push updated operating data incrementally or fully to adjacent distributed nodes (or broadcast to the entire network). After multiple rounds or a single round of network-wide propagation, distributed nodes can automatically synchronize data, ultimately ensuring consistent operating data across all energy devices in the network without the need for a centralized coordination node (such as a centralized lock or arbitration server), significantly improving the scalability and robustness of the distributed energy system.

[0120] Furthermore, after synchronizing the operational data among the distributed nodes, server 104 analyzes the operating status of the energy devices, such as remaining bandwidth, energy device load, and communication latency between energy devices, and then formulates appropriate energy scheduling strategies. It should be noted that in large-scale distributed energy systems, if server 104's computing resources are insufficient, the process of formulating energy scheduling strategies can be distributed across multiple distributed nodes. Each distributed node, based on its known local topology and the operational data of the energy devices obtained through data synchronization, searches for energy scheduling paths. Then, it transmits partial results or intermediate search information from the energy scheduling path search to other distributed nodes through a specific interface for merging, ultimately generating a feasible globally optimal or near-optimal energy scheduling strategy. If server 104 has strong computing power, it can centrally collect the operational data of the energy devices, centrally formulate energy scheduling strategies, and generate corresponding energy scheduling instructions to be broadcast to each energy device for implementation.

[0121] Based on the same inventive concept, this application also provides an energy dispatching device for a distributed energy system to implement the energy dispatching method of the distributed energy system described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the energy dispatching device for a distributed energy system provided below can be found in the limitations of the energy dispatching method for a distributed energy system described above, and will not be repeated here.

[0122] In one embodiment, such as Figure 7 As shown, an energy dispatching device 700 for a distributed energy system is provided, comprising: a data acquisition module 710, a strategy formulation module 720, and a system dispatching module 730, wherein:

[0123] The data acquisition module 710 is used to determine the operating status of each energy device in the distributed energy system based on the operating data of the energy devices after synchronization of each distributed node in the distributed energy system.

[0124] The strategy formulation module 720 is used to determine the energy dispatch strategy that minimizes the preset estimation function based on the operating status of each energy device and the pre-built energy device graph model. The estimation function is used to determine at least one of energy loss and communication delay in the energy dispatch process.

[0125] The system scheduling module 730 is used to schedule energy from a distributed energy system based on energy scheduling strategies.

[0126] The energy device graph model includes multiple nodes and edges connecting the nodes. Nodes represent energy devices, and edges represent the connection relationships between various energy devices in the distributed energy system.

[0127] In one embodiment, the strategy formulation module 720 is further configured to take the starting node in the energy equipment graph model as the current node, with the goal of minimizing a preset valuation function, find a target node in the next level node of the current node that minimizes the preset valuation function, determine the target node as the new current node, return to the step of finding a target node in the next level node of the current node that minimizes the preset valuation function, until an end node is found, obtain the energy dispatch path of the distributed energy system, and determine the energy dispatch strategy of the distributed energy system based on the energy dispatch path.

[0128] In one embodiment, the runtime data includes runtime data of various data structure types, and the data synchronization strategy is different for each type of runtime data.

[0129] In one embodiment, the data structure type includes numeric type, set type, and mapping type. When the data structure type of the running data is numeric type, the data synchronization strategy of the running data is a maximum value strategy per distributed node. When the data structure type of the running data is set type, the data synchronization strategy of the running data is a priority addition strategy or a priority deletion strategy. When the data structure type of the running data is mapping type, the data synchronization strategy of the running data is a key-by-key synchronization strategy.

[0130] In one embodiment, the energy dispatching device 700 of the distributed energy system is further used to periodically update the operating status of each energy device in the distributed energy system, or, when the fluctuation range of the operating status of each energy device is greater than a preset amplitude threshold, update the operating status of each energy device in the distributed energy system, and update the energy dispatching strategy of the distributed energy system based on the updated operating status of each energy device and the pre-built energy device graph model.

[0131] Each module in the energy dispatching device of the aforementioned distributed energy system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0132] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data such as the operating data of synchronized energy devices. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an energy dispatching method for a distributed energy system.

[0133] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0134] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described embodiment of the energy dispatching method for a distributed energy system.

[0135] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in the above-described embodiment of the energy dispatching method for a distributed energy system.

[0136] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described embodiment of the energy dispatching method for a distributed energy system.

[0137] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant regions.

[0138] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0139] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An energy dispatching method for a distributed energy system, characterized in that, The method includes: Based on the operational data of energy devices synchronized across distributed nodes in a distributed energy system, the operational status of each energy device in the system is determined. This operational data includes various data structure types, each with a different data synchronization strategy. The data structure types include numeric, set, and mapping types. When the data structure type is numeric, the data synchronization strategy is a per-distributed-node maximum value strategy, which involves integrating the maximum values ​​of counters on each distributed node in each dimension. When the data structure type is set, the data synchronization strategy is a priority addition strategy or a priority deletion strategy, used to resolve conflicts in adding and deleting the same element's corresponding tags on different distributed nodes. When the data structure type is mapping, the data synchronization strategy is a key-by-key synchronization strategy, which synchronizes each key-value pair according to the data synchronization strategy corresponding to the numeric or set type. Based on the operating status of each energy device and a pre-built energy device graph model, with the goal of minimizing a preset estimation function, an energy scheduling strategy is determined that minimizes the preset estimation function. The estimation function is used to determine at least one of energy loss and communication delay during the energy scheduling process. Based on the energy dispatch strategy, the energy of the distributed energy system is dispatched; The energy device graph model includes multiple nodes and edges connecting each node. Nodes represent energy devices, and the edges of the energy device graph model are used to represent the connection relationships between various energy devices in the distributed energy system.

2. The method according to claim 1, characterized in that, Based on the operating status of each energy device and a pre-constructed energy device graph model, the energy dispatch strategy that minimizes a preset estimation function is determined, including: The starting node in the energy equipment diagram model is taken as the current node; With the goal of minimizing the preset evaluation function, find the target node in the next level node of the current node that minimizes the preset evaluation function, and determine the target node as the new current node; Returning to the step of finding the target node in the next level node of the current node that minimizes the preset evaluation function, the process continues until the end node is found, thus obtaining the energy dispatch path of the distributed energy system. Based on the energy dispatch path, the energy dispatch strategy of the distributed energy system is determined.

3. The method according to claim 1 or 2, characterized in that, After determining the energy dispatch strategy that minimizes the preset estimation function, the method further includes: Regularly update the operating status of each energy device in the distributed energy system; Alternatively, if the fluctuation range of the operating status of each of the energy devices exceeds a preset amplitude threshold, the operating status of each energy device in the distributed energy system is updated. Based on the updated operating status of each energy device and the pre-built energy device graph model, the energy dispatch strategy of the distributed energy system is updated.

4. An energy dispatching system for a distributed energy system, characterized in that, The system includes servers and multiple distributed nodes that are interconnected. The distributed node is used to broadcast the collected operating data of energy equipment in the distributed energy system, and to synchronize the operating data with other distributed nodes besides itself, and upload the synchronized operating data to the server. The server is configured to process the synchronized operating data using the method described in any one of claims 1 to 3, determine an energy scheduling strategy, and schedule the energy of the distributed energy system based on the energy scheduling strategy.

5. An energy dispatching device for a distributed energy system, characterized in that, The device includes: The data acquisition module is used to determine the operating status of each energy device in the distributed energy system based on the operating data of the energy devices after synchronization of each distributed node. The operating data includes operating data of various data structure types, each with a different data synchronization strategy. The data structure types include numeric, set, and mapping types. When the operating data's data structure type is numeric, the data synchronization strategy is a per-distributed-node maximum value strategy, which means integrating the maximum values ​​of the counters on each distributed node in each dimension. When the operating data's data structure type is set, the data synchronization strategy is a priority addition strategy or a priority deletion strategy, which are used to resolve conflicts in adding and deleting the same element's corresponding tags on different distributed nodes. When the operating data's data structure type is mapping, the data synchronization strategy is a key-by-key synchronization strategy, which means synchronizing each key-value pair key-by-key according to the data synchronization strategy corresponding to the numeric or set type. The strategy formulation module is used to determine an energy dispatch strategy that minimizes a preset estimation function based on the operating status of each energy device and a pre-built energy device graph model. The estimation function is used to determine at least one of energy loss and communication delay in the energy dispatch process. The system scheduling module is used to schedule the energy of the distributed energy system based on the energy scheduling strategy. The energy device graph model includes multiple nodes and edges connecting each node. Nodes represent energy devices, and the edges of the energy device graph model are used to represent the connection relationships between various energy devices in the distributed energy system.

6. The apparatus as claimed in claim 5, characterized in that, The strategy formulation module is further configured to: take the starting node in the energy equipment graph model as the current node; with the goal of minimizing a preset evaluation function, find the target node in the next level node of the current node that minimizes the preset evaluation function, and determine the target node as the new current node; return to the step of finding the target node in the next level node of the current node that minimizes the preset evaluation function, until the end node is found, to obtain the energy dispatch path of the distributed energy system; and determine the energy dispatch strategy of the distributed energy system based on the energy dispatch path.

7. The apparatus as described in claim 5 or 6, characterized in that, The energy dispatching device of the distributed energy system is also used to periodically update the operating status of each energy device in the distributed energy system; or, when the fluctuation range of the operating status of each energy device is greater than a preset amplitude threshold, update the operating status of each energy device in the distributed energy system; and update the energy dispatching strategy of the distributed energy system based on the updated operating status of each energy device and the pre-constructed energy device graph model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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