Distributed resource interaction methods, terminals, devices and media for autonomous distribution zones

Abstract

This invention relates to a distributed resource interaction method, terminal, device, and medium for distribution transformer area autonomy. The method includes the following steps: determining a decentralized autonomous architecture based on the distribution transformer area resource management scenario and determining the master node using a self-organizing method; if the master node is fixed, it is selected based on the transformer area topology and / or the transformer area governance responsibility; if the master node is dynamically selected, it is elected in real-time by election nodes according to the resource management scenario requirements; the master node regulates the election nodes and follower nodes based on node decision indicators, which are calculated by the interaction terminal based on the electrical data of the corresponding distributed resource devices. This invention achieves plug-and-play interactive control of distributed resources in low-voltage transformer areas, improving the convenience, effectiveness, real-time performance, and reliability of distributed resource regulation.

Description

Technical Field

[0001] This invention relates to the field of distributed resource coordination and control technology, and in particular to distributed resource interaction methods, terminals, devices and media for autonomous distribution areas. Background Technology

[0002] With the large-scale integration of distributed resources such as new energy power generation, energy storage devices, and flexible loads into distribution substations, the distribution network has transformed from a traditional unidirectional power supply mode into a multi-source interactive active network. However, the randomness, intermittency, and heterogeneity of the high proportion of distributed resources pose a severe challenge to the autonomous capabilities of distribution substations. Existing technologies have the following shortcomings in terms of resource interaction control and terminal compatibility: First, the communication protocols of distributed resource devices are highly heterogeneous. Existing terminals lack adaptive protocol recognition and dynamic mapping capabilities, rely on manual configuration of point tables, resulting in low access efficiency, insufficient security protection, and inability to achieve resource interconnection and interoperability. Second, traditional control adopts centralized decision-making at the main station, which requires frequent interaction with terminals and massive amounts of data, easily leading to communication congestion and response delays. The existing edge layering strategy relies on centralized management and control of converged terminals, but the access capacity of converged terminals is limited. Expanding or upgrading hardware will significantly increase costs and make it difficult to meet the second-level response requirements of the area governance. Third, the micro-autonomous entities formed around edge terminals lack a distributed global coordination mechanism, can only achieve local autonomy, and cannot coordinate comprehensive governance goals such as the safety and economy of the power station area. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a distributed resource interactive control method, a plug-and-play interactive terminal, an electronic device and a storage medium for autonomous distribution substations, which can realize plug-and-play interactive control of distributed resources in low-voltage substations and improve the convenience, effectiveness, real-time performance and reliability of distributed resource regulation.

[0004] The technical solution adopted by this invention to solve its technical problem is as follows: A distributed resource interaction control method for autonomous distribution substations is provided. The distribution substation is equipped with interactive terminals that are communicatively connected to and correspond one-to-one with distributed resource devices. The interactive terminals interact with their corresponding distributed resource devices to obtain electrical information and are divided into master nodes, election nodes, and follower nodes according to their governance responsibilities. The method includes the following steps: Based on the scenario of distribution network area resource management, a decentralized autonomous architecture is determined, and a corresponding self-organizing method is adopted to determine the master node: If a decentralized autonomous architecture with a fixed master node is adopted, the master node is selected based on the transformer area topology and / or the transformer area governance responsibility. If a decentralized autonomous architecture with dynamic master node selection is adopted, the master node is elected in real time by the election node according to the resource management scenario requirements. The master node regulates the election and follower nodes based on node decision indicators; these node decision indicators are calculated by the interactive terminal based on the electrical information it acquires.

[0005] Furthermore, based on the scenario of distribution area resource management, a decentralized autonomous architecture is determined, including: When dealing with reverse heavy overload scenarios, a distributed autonomous architecture with a fixed master node is adopted; When dealing with scenarios involving voltage over-limits or integrated coordinated control of power grid, source, load, and energy storage in a distribution area, a decentralized autonomous architecture with dynamic selection of master nodes is adopted.

[0006] Furthermore, when considering the integrated coordination and control scenario of power generation, grid, load, and storage in a distribution area, the decision-making indicators for nodes also include economic indicators.

[0007] Furthermore, the economic metrics include the sum of the net revenues of all distributed resource devices involved in governance.

[0008] Furthermore, based on the needs of resource management scenarios, the election nodes elect a master node in real time, including: The election node configures the weights of each node's decision-making indicators based on the distribution area resource management scenario, and calculates their weighted sum as its own master node selection indicator. While broadcasting governance topics, the election node listens to the governance topics published by other election nodes and compares its own master node selection index with the listened master node selection index. The election node with the higher master node selection index is set as the proxy node to continue broadcasting governance topics, while the election node with the lower master node selection index stops publishing its own governance topics. This step is repeated until the election node with the highest master node selection index is elected as the master node.

[0009] Furthermore, the node decision indicators include at least one of the following: voltage over-limit index, voltage sensitivity index, overload risk index, and net output ratio index.

[0010] Furthermore, when dealing with voltage over-limit scenarios, the weights of the voltage over-limit severity index, voltage sensitivity index, and overload risk index are increased.

[0011] Furthermore, when considering the integrated coordination and control scenario of power generation, grid, load, and storage in the power distribution area, the weight of the net power output index of the nodes is increased, and economic indicators are used as the optimization target.

[0012] Furthermore, the voltage exceedance level index is expressed as follows:

[0013] in, branch road i Middle node j In the tThe degree of voltage exceeding the limit at any given time. branch road i Middle node j In the t The voltage exceeds the limit at any given time. This is the estimated minimum voltage value after exceeding the limit. This represents the estimated maximum voltage value after exceeding the limit.

[0014] Furthermore, the voltage sensitivity index is expressed as:

[0015] in, For voltage sensitivity indicators, branch road i Middle node j The rated apparent capacity, and Branch roads i Middle node j The active real-time voltage sensitivity and reactive real-time voltage sensitivity.

[0016] Furthermore, the overload risk indicator is expressed as:

[0017] in, As an overload risk indicator, and Representing branch roads i Middle node j In the t Active power and reactive power at any given time. Indicates a branch i or transformer k The rated apparent power.

[0018] Furthermore, the net output ratio is expressed as follows:

[0019] in, Indicates a branch i Middle node j In the t Real-time power generation at any given moment. Indicates a branch i Middle node j In the t Real-time load at any given moment.

[0020] This invention also provides a distributed resource plug-and-play interactive terminal for autonomous distribution radio areas, comprising: The communication interface module is connected to the distributed resource device and is used to exchange communication messages with the distributed resource device. The dynamic protocol feature library is used to store various communication protocol feature vectors of multi-brand distributed resource devices. The communication protocol feature vectors are obtained by extracting the temporal features of the messages using a temporal convolutional network. The protocol self-identification module is used to identify the protocol type and its confidence level based on the photovoltaic inverter response message using a protocol identification model built on a time-series convolutional network. When the confidence level is less than a set threshold, the protocol identification model is updated online based on the clustering of unidentified messages. The control module is used to send probe messages to the distributed resource device according to the identified protocol type, receive the response messages returned by the distributed resource device and parse them to obtain the function code and data code, wherein the data code includes the electrical data returned by the distributed resource device; The judgment module is used to determine whether the recognition was successful based on the confidence level, function code, and data code. The decentralized autonomous control module is used to determine the decentralized autonomous architecture based on the resource management scenario of the distribution area, and then adopt the corresponding self-organizing method to determine its own governance responsibilities and execute corresponding control operations to achieve plug-and-play interactive autonomy of distributed resources.

[0021] Furthermore, it also includes: The online operation and maintenance module is used for remote configuration and management, status self-diagnosis and reporting, and log recording; The security encryption module is used to encrypt data transmission and verify received data.

[0022] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the distributed resource interaction control method for autonomous distribution substations as described above.

[0023] The present invention also provides 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 distributed resource interaction control method for autonomous distribution radio areas as described above.

[0024] Beneficial effects By adopting the above-mentioned technical solution, the present invention has the following advantages and positive effects compared with the prior art: This invention designs two types of decentralized autonomous architectures to meet the differentiated governance needs of distribution areas. It relies on interactive terminals that are bound one-to-one with distributed resources to achieve localized data collection, decision-making and control. It does not need to rely on a main station or converged terminal to centrally forward data, thus avoiding the risk of communication congestion and meeting the real-time governance requirements of different scenarios. This invention constructs a hierarchical collaboration model of master node, election node, and follower node, which not only preserves the autonomous operation capability of distributed resources and avoids the rigidity of passively receiving instructions, but also achieves global collaboration by uniformly allocating governance tasks and regulating cluster operation through the master node, thus solving the problem of disordered governance of heterogeneous resources and significantly improving the effectiveness of distributed resource management. This invention adopts a fixed master node architecture for reverse heavy overload scenarios, directly selecting the topology center of the transformer area or the node with the heaviest governance responsibility to coordinate, skipping the election process and greatly reducing governance response time; for voltage over-limit or source-grid-load-storage scenarios, a dynamic master node architecture is adopted, which does not rely on the converged terminal, and the interactive terminal self-organizes the election, which avoids centralized communication congestion and does not require additional hardware expansion, thus greatly reducing governance costs. This invention constructs a multi-indicator system covering safety, efficiency, and economic dimensions. Addressing the differentiated governance needs of voltage exceedance and power generation-grid-load-storage coordination scenarios, it assigns scenario-specific weights to each indicator. This significantly improves the coordinated utilization rate of power generation-grid-load-storage resources while thoroughly resolving local voltage exceedance issues and solidifying the bottom line for safe operation. Furthermore, prioritizing safety-related indicator weights for voltage exceedance scenarios ensures rapid identification of nodes with the greatest impact on global voltage regulation. For integrated power generation-grid-load-storage coordination scenarios, it balances safety and economic indicator weights, selecting the optimal node that ensures safe operation of the distribution area while maximizing overall benefits, accurately adapting to the localized voltage fluctuations and power imbalances in the distribution area. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the distributed autonomous architecture of the distribution radio station area with a fixed master node according to the second embodiment of the present invention; Figure 2 This is a schematic diagram of the distributed autonomous architecture of the distribution radio station area with dynamic selection of the master node according to the second embodiment of the present invention. Figure 3 This is a schematic diagram of the basic role transformation of the distributed resource plug-and-play interactive terminal according to the first and second embodiments of the present invention; Figure 4 This is a schematic diagram illustrating the initiation of a distributed autonomous control strategy for multiple scenarios of distributed resources in a distribution radio station according to the first and second embodiments of the present invention. Figure 5 This is a flowchart of the dynamic election process for the master node according to the second embodiment of the present invention; Figure 6 This is a schematic diagram of the hybrid protocol identification model structure according to the first embodiment of the present invention; Figure 7 This is a schematic diagram of the protocol message incremental learning process according to the first embodiment of the present invention; Figure 8This is a schematic diagram of the self-organizing network control of the distributed resource plug-and-play interactive terminal according to the first embodiment of the present invention; Figure 9 This is a schematic diagram of the structure of the third embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of the fourth embodiment of the present invention. Detailed Implementation

[0026] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0027] The first embodiment of this invention relates to a distributed resource interaction method for distribution transformer area autonomy. It aims to achieve distributed resource interaction control in low-voltage distribution transformer areas based on a distributed autonomous control architecture, improving the effectiveness, convenience, real-time performance, and reliability of distributed resource regulation. This effectively solves problems such as reverse overload in distribution transformer areas and voltage exceeding limits at user grid connection points, thereby enhancing the safe and efficient power supply capability of distribution transformer areas. In this embodiment, the distribution transformer area is equipped with interactive terminals that are communicatively connected to and correspond one-to-one with the distributed resource devices. These interactive terminals are plug-and-play terminals as described in the first embodiment. The interactive terminals interact with their corresponding distributed resource devices to obtain electrical information and are classified into master nodes, election nodes, and follower nodes according to their governance responsibilities.

[0028] This implementation proposes a decentralized autonomous architecture for distributed resources in a distribution network. This architecture does not emphasize a unified, centralized organization to define governance responsibilities. It allows distributed resource entities participating in event response to make their own decisions, allows distributed resources to proactively adjust based on their own operating conditions, allows distributed resource entities to engage in mutual competition based on their own needs, and allows distributed resources to passively receive control commands to adjust their operating status. Together with other distributed resources participating in the same batch of event responses, it achieves governance goals, reaching a decentralized autonomous state. Based on the method for selecting the master node of the distributed resource cluster participating in a certain batch of events, it is further divided into two types: a decentralized autonomous architecture with a fixed master node and a decentralized autonomous architecture with dynamically selected master nodes.

[0029] Decentralized autonomous architecture with fixed master nodes, such as Figure 1 As shown, the core power supply of the transformer substation connects all nodes within the substation via low-voltage lines, forming the basic power supply network. Flexible resources are distributed throughout the substation, covering different line nodes, and may specifically include one or more of the following distributed resources: Distributed photovoltaic power; Distributed energy storage; Electric vehicle charging stations; Controllable load.

[0030] Under this architecture, the distributed resource located at the center of the distribution area or the distributed resource with the heaviest governance responsibility acts as the master node, which can issue control instructions to other distributed resources and coordinate the resources of the entire distribution area to quickly achieve active power balance.

[0031] A decentralized autonomous architecture that dynamically selects master nodes, such as Figure 2 As shown, this architecture does not determine a fixed master node based on preset rules. Instead, it dynamically elects the node with the best adaptability to the current governance scenario as the master node through self-organization. The remaining nodes establish management and control connections with the master node to form an autonomous cluster. The master node is only valid in the current governance cycle. If the scenario changes or the master node communication fails, the cluster can quickly trigger a new round of election. The entire process relies on the distributed self-organization of the terminal's local computing power, adapting to the dynamic working conditions of complex distribution areas.

[0032] This implementation proposes a distributed autonomous control strategy for distributed resources in distribution substations across multiple scenarios, utilizing a distributed autonomous architecture. This enables flexible distributed resources within the distribution substation, such as distributed photovoltaics, electric vehicles, controllable loads, and distributed energy storage, to spontaneously organize networks, exchange information, and collaborate between one or more types of devices or between multiple devices without relying on network concentrators or intermediate controllers. By establishing a distributed resource interoperability self-organizing network information model, developing interoperability self-organizing network specification mapping protocols, and defining interoperability self-organizing network standard procedures, seamless integration and free data flow of the distribution substation autonomous system are achieved, thereby improving the flexibility and real-time performance of business collaboration.

[0033] In the process of achieving decentralized autonomous control through distributed resource interoperability ad hoc networks, distributed resource plug-and-play interactive terminals are used for interactive control. Each distributed resource plug-and-play interactive terminal is assigned a basic role in the distributed autonomous system of the distribution area: master node, follower node, and election node. When a distributed resource plug-and-play interactive terminal joins a governance cluster, it can only assume one basic role. The basic role transitions between different distributed resource plug-and-play interactive terminals are as follows: Figure 3 As shown.

[0034] For reverse heavy overload scenarios, since all distributed photovoltaic and energy storage devices in the entire distribution area need to participate in the active power output optimization, a decentralized governance mode with fixed master nodes is adopted, and the distributed resources located in the center of the distribution area or the distributed resources with the heaviest governance responsibility are set as master nodes.

[0035] For scenarios involving voltage overruns or integrated coordination and control of power generation, grid, load, and energy storage in a distribution area, since the voltage overruns or power balance situations at different nodes in the distribution area are not entirely consistent, dynamic selection of the leading node is used to organize the governance group, issue tasks, manage the governance process, and report governance results.

[0036] The distributed resource decentralized autonomous control strategy proposed in this embodiment achieves interoperability networking through distributed resource plug-and-play interactive terminals. The autonomous strategy initiation process is as follows: Figure 4 As shown, when a distributed resource plug-and-play interactive terminal autonomously detects a governance need, it can determine whether an autonomous cluster already exists within the area through network monitoring. If an autonomous cluster exists, it applies to join the cluster; if no autonomous cluster exists, it publishes an interoperability request and topic to dynamically select a master node from other nodes with self-organizing network needs. When a distributed resource plug-and-play interactive terminal generates an active governance need, its basic role changes from a follower node to an election node.

[0037] The master node, elected nodes, and follower nodes collaborate to achieve the goal of power distribution area governance. The master node makes comprehensive decisions based on real-time data such as the electrical parameters, voltage sensitivity, net power generation, and load factor of the elected nodes, determining the governance responsibility allocation between the elected nodes and potential follower nodes, and then uniformly regulating both. The master node is responsible for real-time assessment of whether the elected nodes are utilizing their full capacity in the autonomous governance process. If governance fails to meet targets and the elected nodes still have remaining adjustment capacity, the master node can modify the governance group's droop coefficient or directly issue adjustment commands to encourage the elected nodes to fully utilize their remaining capacity. If the master node determines that relying solely on the elected nodes cannot achieve the governance goal, it can further invite potential follower nodes to participate in the governance task after autonomous judgment and decision-making.

[0038] The comprehensive decision-making indicators for decentralized autonomous control strategies include voltage limit exceedance index, voltage sensitivity index, overload risk index, and net output ratio index, which are calculated by the interactive terminal based on the electrical data of its corresponding distributed resource equipment.

[0039] The comprehensive decision-making index for decentralized autonomous control strategy is calculated as follows: Voltage Exceedance Index Real-time measurement of branch i ,node j No. t The effective voltage value at time t is According to the national standard requirements for safe operation of low-voltage distribution transformer substation node voltage, its range must be controlled between -10% and +7%. Voltage exceeding the limit is prohibited. It is calculated from equation (1).

[0040] (1) In the formula, This is the reference value for the voltage in the transformer substation area.

[0041] After standardizing the voltage exceedance values ​​to their maximum and minimum values, we obtain the voltage exceedance severity index. As shown in equation (2).

[0042] (2) In the formula, To determine the minimum possible voltage after exceeding the limit, it is usually taken as... When the voltage exceeds the upper limit Take the voltage exceeding the limit when the transformer area is lightly loaded, and when the voltage is below the lower limit... Take the voltage limit value when the transformer area is under heavy load.

[0043] Voltage sensitivity index Voltage sensitivity determines the extent to which changes in active and reactive power at a node affect voltage. Higher voltage sensitivity indicates a greater impact of changes in active and reactive power on the node voltage, and the node should bear more responsibility for voltage management in the distribution area.

[0044] Define the area Real-time voltage sensitivity of distributed resource devices within the system (active / reactive power) , It can be calculated according to equation (3): (3) In the formula, , These represent the real-time active and reactive power changes of the distributed resource devices, with the corresponding voltage changes being... , subscript P , Q They represent meritorious and unmeritorious achievements, respectively. i , j Representing branch roads i With nodes j , t Indicates the time.

[0045] Therefore, a standardized voltage sensitivity index is defined. As shown in equation (3).

[0046] (4) In the formula, Represented as a branch road i ,node j The rated apparent capacity of distributed resources such as photovoltaic inverters and distributed energy storage.

[0047] Overload risk indicators According to real-time monitoring of branch linesi ,node j No. t The active and reactive power at any given time are used to calculate the load rate of the line or distribution transformer, and after standardization, an overload risk index is obtained. As shown in equation (5) (5) In the formula, , Representing branch roads i ,node j No. t Active and reactive power at any given time Indicates a branch i or transformer k The rated apparent power. When heavy load and overload conditions occur, i.e. hour, For the overload judgment value (such as when the distribution transformer is overloaded when it is greater than 80% as specified in the national standard), a decentralized governance mode with fixed master nodes is adopted, and the distributed resource located in the center of the distribution area (the distribution transformer is the center of the distribution area) or the distributed resource with the heaviest governance responsibility is set as the master node.

[0048] Net output ratio index Define branches i ,node j The distributed resources are monitored in real time to obtain the net output index as follows: As shown in equation (6), only considering > The situation.

[0049] (6) In the formula, Indicates a branch i ,node j Photovoltaic inverters, distributed energy storage and other distributed resources t Real-time power generation at any given moment. Indicates a branch i ,node j No. t Real-time load at any given moment.

[0050] Master node selection criteria because , , , , All data are dimensionless standardized data. Based on this, and taking into account factors such as voltage limit exceedance, voltage sensitivity variation, overload risk, node net output ratio, and economic efficiency, a comprehensive selection index for master nodes is proposed. , represented as (7) In the formula, They are respectively , , , The weighting coefficients to be set.

[0051] The weights of various indicators selected by the master node differ depending on the application scenario. When dealing with scenarios that affect the safe operation of the power grid, such as voltage overruns, the decision will prioritize voltage indicators and quickly call upon the nearest distributed resources for support. The weights are heavily biased towards safety, with voltage overrun indicators, voltage sensitivity indicators, and overload risk indicators having larger weight coefficients.

[0052] When facing the integrated coordination and control scenario of power generation, grid, load and storage in the transformer area, the decision-making will take into account both economic and safety indicators. Under the premise of ensuring safety margin, the net load curve will be smoothed in the way with the lowest cost or the highest efficiency. At this time, the net output index of the node is relatively large, and the economic indicators are used as the optimization goal to maximize the benefits.

[0053] Economic indicators: Taking into account the charging and discharging losses, equipment depreciation, and maintenance costs of distributed resources in the distribution area (such as energy storage and V2G for electric vehicles), as well as the electricity cost savings or net benefits brought by participating in demand response and peak shaving and valley filling, the economic indicators are calculated with a 24-hour period as the net benefit calculation period: (8) ① Electric vehicles V2G equipment revenue includes demand response compensation revenue and electricity sales revenue, as shown in the following formula.

[0054] (9) In the formula: for t V2G electricity sales price in the designated area; To compensate for the price; , They are respectively t Time Branch i ,node j The charging power and discharging power of electric vehicles.

[0055] Electric vehicle operating costs It includes depreciation costs and maintenance costs, as shown in the following formula.

[0056] (10) In the formula: The cost coefficient for electric vehicles participating in operation; , The power output of electric vehicles and its maximum value.

[0057] ② Distributed photovoltaic The revenue from distributed photovoltaic power generation in the area is shown below: (11) In the formula: The on-grid tariff for distributed photovoltaic power in the transformer substation; For time t, the branch road of the platform area i ,node j The power generation capacity of the photovoltaic inverter; Distributed photovoltaic operating costs per day This includes depreciation costs, maintenance costs, and waste costs, as shown in the following formula.

[0058] (12) In the formula: This refers to the cost coefficient for distributed photovoltaic curtailment. , This refers to the actual and maximum output of distributed photovoltaic power. Assuming that the depreciation and maintenance costs of distributed photovoltaic systems are fixed values, the calculation is as shown in the following formula.

[0059] (13) In the formula: Maintenance rate of distributed photovoltaic equipment; C PV,unit Cost per unit capacity of distributed photovoltaic inverters; For the capacity of distributed photovoltaic inverters; This refers to the number of operating days throughout the entire lifecycle of a distributed photovoltaic inverter.

[0060] ③ Distributed energy storage The revenue from distributed energy storage in the distribution area includes demand response compensation revenue and peak-valley arbitrage electricity price revenue, as shown in the following formula.

[0061] (14) In the formula: for t The electricity sales price of distributed energy storage in the designated area at any given time; To compensate for the price; , They are respectively t Time Branch i ,node j The charging and discharging power of distributed energy storage.

[0062] Distributed energy storage operating costs per day This includes depreciation costs and maintenance costs, as shown in the following formula.

[0063] (15) In the formula: , , These are the coefficients of the distributed energy storage operation cost function; This refers to distributed energy storage power.

[0064] The master node selection process is as follows: Figure 5 As shown. First, by the election node ( i , j The governance topic is broadcast, and then it is determined whether the topic has been elected by the node. m , n Subscription. Node ( m , n ) Listened to the election node ( i , j The governance themes released proactively select indicators from their own main nodes. With the election node being monitored ( i , j ) If a comparison is made, Then it is determined by the election node ( i , j Subscription node ( m , n ) topic, and stop publishing its own topic, confirming that it has been blocked by the node ( m , n The proxy switches to listening mode for communication. If... Then the election node ( i , j ) becomes the proxy node of node (m,n), node ( i , j Continue broadcasting the governance topic, determine if it has been subscribed to by other election nodes, and repeat this step until an election node is confirmed. l , k ) becomes a proxy node for other election nodes, and nominates election nodes ( l , k ) becomes the master node. Election node ( i , j When broadcasting governance topics, a listening function is simultaneously activated to actively monitor whether other election nodes are broadcasting governance topics. Nodes that have already subscribed to governance topics of other election nodes cannot subscribe to additional topics. If the master node loses communication, the election nodes can re-elect a new master node. Even if the original master node resumes normal communication, the new master node does not need to transfer its roles and permissions to the original master node; the original master node automatically becomes an election node in the governance cluster.

[0065] The second embodiment of the present invention relates to a plug-and-play interactive terminal for distributed resources in autonomous distribution substations, used to implement the above-mentioned distributed resource interactive control method. It aims to achieve plug-and-play interactive control of distributed resources in low-voltage substations without relying on the actual topology information of the substation, thereby improving the effectiveness, convenience, real-time performance and reliability of distributed resource regulation.

[0066] The distributed resource plug-and-play interactive terminal of this embodiment has protocol recognition, decentralized autonomous control, online operation and maintenance and security encryption functions. It can autonomously identify the manufacturers and models of different distributed resource devices, autonomously match their communication protocols and point tables, support telemetry, remote signaling, remote control and remote adjustment capabilities, support real-time data storage and processing capabilities at the second level, and its control strategy is implemented through a program.

[0067] (1) Protocol identification A communication interface module is used to connect to the distributed resource device to be identified, and to realize the communication connection with the distributed resource device to be identified. A dynamic protocol feature library is used to store various communication protocol feature vectors of distributed resource devices from multiple brands. These feature vectors are generated by extracting message temporal features through a dedicated temporal convolutional network (TCN). The protocol self-identification module mainly includes a deep learning-based protocol self-identification unit and an incremental protocol self-learning unit.

[0068] Among them, the protocol self-identification unit based on deep learning adopts a hybrid protocol identification model composed of TCN and multi-head attention mechanism, such as... Figure 6 As shown, the input information for the self-identification unit is the inverter response message, and the output information is the protocol type and confidence level. Unlike traditional protocol identification algorithms that rely on simple static rule matching, this model uses deep learning to dynamically extract and classify protocol features, significantly improving the accuracy of complex protocol identification in various environments. The hybrid protocol identification model, composed of TCN and multi-head attention mechanisms, consists of an input encoding layer, a dilated causal convolutional layer, a communication protocol-specific normalization layer, a multi-head attention layer, and a protocol identification and classification output layer. The input encoding layer converts the protocol message byte sequence into a high-dimensional vector containing positional features through multidimensional convolution. For example, if the input is a 1-dimensional vector, the output is an n-dimensional vector (n=16). The dilated causal convolution layer extracts the long sequence dependency of the message by combining causal convolution and dilated convolution, expanding the n-dimensional vector into an m-dimensional vector (m=128). Then, the normalization coefficient is selected according to the communication protocol length to obtain the communication protocol-specific normalization result, so that the model can adaptively handle messages of different lengths. The weights of the communication protocol-specific normalization result are visualized as protocol key fields using a multi-head attention layer, and the interpretable proof of the protocol key fields is obtained. Finally, the Softmax unit of the co-identification classification output layer is used to calculate the photovoltaic inverter protocol type and its confidence interval.

[0069] The specific method for training and testing the network parameters of the hybrid protocol recognition model consisting of TCN and multi-head attention mechanism is as follows: The dynamic protocol feature library is divided into training and testing sets according to 70% and 30% respectively. The global loss function of the model is...

[0070] in, Cross-entropy loss is used to measure the difference between the probability distribution of the protocol classification result and the true label. To compare losses, the similarity between different protocols can be calculated. , Hyperparameters can be set.

[0071] The model is trained using a dynamic protocol feature library training set, with the protocol type as the output. Protocol messages are passed layer by layer to the output layer. The gradient is calculated using backpropagation, and the Adam optimizer is selected to update the model parameters. This training process is repeated multiple times until the loss function converges, thus completing the model training. The model weights and structure at the end of training are saved. The test dataset is then input into the model, and the protocol classification output is calculated. The accuracy of the model is comprehensively evaluated by calculating performance metrics such as mean squared error and mean absolute error for protocol classification.

[0072] like Figure 7 As shown, the incremental protocol self-learning unit can trigger an online update of the model based on unidentified message clustering when the confidence level obtained by the protocol self-identification unit is lower than the threshold, thus solving the problem of model failure caused by the launch of new protocols.

[0073] The control module is used to send probe messages to distributed resource devices based on a dynamic protocol signature library and to receive the identification results from the protocol self-identification module. The probe message is a special address data request message sent by the distributed resource plug-and-play interactive terminal to the distributed resource device to be identified. The identification result is obtained by parsing the response message returned by the distributed resource device to obtain the function code and data code. The judgment module comprehensively judges the confidence level, function code, and data code to obtain the specific result of whether the protocol self-identification module has successfully identified the protocol.

[0074] (2) Decentralized autonomous control Based on the distributed autonomous architecture of the distribution radio area proposed in this embodiment, the plug-and-play interactive terminal for distributed resources in this embodiment has multi-scenario distributed autonomous control functions for distributed resources in the distribution radio area. For different governance scenarios, it selects a fixed / dynamic master node networking strategy through a self-organizing network mode to achieve plug-and-play interactive autonomy for distributed resources. Figure 8As shown. Simultaneously, a rollback judgment function is added to the local control process of distributed resources. When the voltage value of the current sampling point is lower than the voltage value of the previous sampling point, but higher than the voltage threshold for exiting power regulation, the power output value will still maintain the control value of the previous sampling point. This avoids frequent power adjustments of distributed resources caused by short-term voltage fluctuations due to cloudy weather or other factors, thus affecting the lifespan of the equipment.

[0075] (3) Online operation and maintenance The distributed resource plug-and-play interactive terminal proposed in this embodiment has online operation and maintenance functions. It achieves wireless connection with the module through Bluetooth Low Energy (BLE) technology, providing field engineers with convenient means for equipment configuration, status monitoring and fault diagnosis.

[0076] Remote configuration and management: Supports remote parameter configuration, software upgrades, restarts and resets of terminals via a secure network channel.

[0077] Status self-diagnosis and reporting: The terminal continuously monitors its own and connected devices' working status (such as communication quality, device fault alarms, etc.) and periodically reports diagnostic information to the operation and maintenance platform.

[0078] Log recording: Records detailed logs of terminal operation, actions, alarms, etc., and supports remote query and download, facilitating fault tracing and analysis.

[0079] (4) Secure encryption By employing national cryptographic algorithms and dedicated security chip technology, a security protection system covering the entire process of data transmission, storage, and interaction is constructed.

[0080] Encrypted transmission: On top of the TLS tunnel, critical business data (especially remote control and telemetry commands) can be encrypted again at the application layer (such as the national cryptographic SM4 algorithm) to achieve "double encryption". Even if the TLS layer is cracked, the business data remains secure.

[0081] Message Authentication Code: For each important data message or command, a MAC value is generated using a key hashing algorithm (such as HMAC-SM3) and appended to the message. The receiver recalculates and compares the MAC using the same key; any tampering with the message will cause the verification to fail.

[0082] This implementation proposes a distributed autonomous control strategy for distributed resources in distribution substations across multiple scenarios, utilizing a distributed autonomous architecture. This enables flexible distributed resources within the distribution substation, such as distributed photovoltaics, electric vehicles, controllable loads, and distributed energy storage, to spontaneously organize networks, exchange information, and collaborate between one or more types of devices or between multiple devices without relying on network concentrators or intermediate controllers. By establishing a distributed resource interoperability self-organizing network information model, developing interoperability self-organizing network specification mapping protocols, and defining interoperability self-organizing network standard procedures, seamless integration and free data flow of the distribution substation autonomous system are achieved, thereby improving the flexibility and real-time performance of business collaboration.

[0083] In the process of achieving decentralized autonomous control through distributed resource interoperability ad hoc networks, distributed resource plug-and-play interactive terminals are used for interactive control. Each distributed resource plug-and-play interactive terminal is assigned a basic role in the distributed autonomous system of the distribution area: master node, follower node, and election node. When a distributed resource plug-and-play interactive terminal joins a governance cluster, it can only assume one basic role. The basic role transitions between different distributed resource plug-and-play interactive terminals are as follows: Figure 3 As shown.

[0084] For reverse heavy overload scenarios, since all distributed photovoltaic and energy storage devices in the entire distribution area need to participate in the active power output optimization, a decentralized governance mode with fixed master nodes is adopted, and the distributed resources located in the center of the distribution area or the distributed resources with the heaviest governance responsibility are set as master nodes.

[0085] For scenarios involving voltage overruns or integrated coordination and control of power generation, grid, load, and energy storage in a distribution area, since the voltage overruns or power balance situations at different nodes in the distribution area are not entirely consistent, dynamic selection of the leading node is used to organize the governance group, issue tasks, manage the governance process, and report governance results.

[0086] The distributed resource decentralized autonomous control strategy proposed in this embodiment achieves interoperability networking through distributed resource plug-and-play interactive terminals. The autonomous strategy initiation process is as follows: Figure 4 As shown, when a distributed resource plug-and-play interactive terminal autonomously detects a governance need, it can determine whether an autonomous cluster already exists within the area through network monitoring. If an autonomous cluster exists, it applies to join the cluster; if no autonomous cluster exists, it publishes an interoperability request and topic to dynamically select a master node from other nodes with self-organizing network needs. When a distributed resource plug-and-play interactive terminal generates an active governance need, its basic role changes from a follower node to an election node.

[0087] The master node, elected nodes, and follower nodes collaborate to achieve the goal of power distribution area governance. The master node makes comprehensive decisions based on real-time data such as the electrical parameters, voltage sensitivity, net power generation, and load factor of the elected nodes, determining the governance responsibility allocation between the elected nodes and potential follower nodes, and then uniformly regulating both. The master node is responsible for real-time assessment of whether the elected nodes are utilizing their full capacity in the autonomous governance process. If governance fails to meet targets and the elected nodes still have remaining adjustment capacity, the master node can modify the governance group's droop coefficient or directly issue adjustment commands to encourage the elected nodes to fully utilize their remaining capacity. If the master node determines that relying solely on the elected nodes cannot achieve the governance goal, it can further invite potential follower nodes to participate in the governance task after autonomous judgment and decision-making.

[0088] The comprehensive decision-making indicators for decentralized autonomous control strategies include voltage limit exceedance index, voltage sensitivity index, overload risk index, and net output ratio index, which are calculated by the interactive terminal based on the electrical data of its corresponding distributed resource equipment.

[0089] The weights of various indicators selected by the master node differ depending on the application scenario. When dealing with scenarios that affect the safe operation of the power grid, such as voltage overruns, the decision will prioritize voltage indicators and quickly call upon the nearest distributed resources for support. The weights are heavily biased towards safety, with voltage overrun indicators, voltage sensitivity indicators, and overload risk indicators having larger weight coefficients.

[0090] When facing the integrated coordination and control scenario of power generation, grid, load and storage in the transformer area, the decision-making will take into account both economic and safety indicators. Under the premise of ensuring safety margin, the net load curve will be smoothed in the way with the lowest cost or the highest efficiency. At this time, the net output index of the node is relatively large, and the economic indicators are used as the optimization goal to maximize the benefits.

[0091] The process of selecting the master node is as follows. First, the election node ( i , j The governance topic is broadcast, and then it is determined whether the topic has been elected by the node. m , n Subscription. Node ( m , n ) Listened to the election node ( i , j The governance themes released proactively select indicators from their own main nodes. With the election node being monitored ( i , j ) If a comparison is made, Then it is determined by the election node ( i , j Subscription node ( m , n) topic, and stop publishing its own topic, confirming that it has been blocked by the node ( m , n The proxy switches to listening mode for communication. If... Then the election node ( i , j ) becomes the proxy node of node (m,n), node ( i , j Continue broadcasting the governance topic, determine if it has been subscribed to by other election nodes, and repeat this step until an election node is confirmed. l , k ) becomes a proxy node for other election nodes, and nominates election nodes ( l , k ) becomes the master node. Election node ( i , j When broadcasting governance topics, a listening function is simultaneously activated to actively monitor whether other election nodes are broadcasting governance topics. Nodes that have already subscribed to governance topics of other election nodes cannot subscribe to additional topics. If the master node loses communication, the election nodes can re-elect a new master node. Even if the original master node resumes normal communication, the new master node does not need to transfer its roles and permissions to the original master node; the original master node automatically becomes an election node in the governance cluster.

[0092] A third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the distributed resource interaction control method for autonomous distribution substations as described above.

[0093] A structural block diagram of an electronic device 100 provided in this embodiment is shown below. Figure 9 As shown, it may include one or more of the following components: processor 110, memory 120, screen 130, and one or more applications, wherein the one or more applications may be stored in memory 120 and configured to be executed by one or more processors 110, and the one or more applications are configured to perform the methods as described in the foregoing method embodiments.

[0094] The processor 110 may include one or more processing cores. The processor 110 connects to various parts within the electronic device 100 using various interfaces and lines, and performs various functions and processes data of the electronic device 100 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 120, and by calling data stored in the memory 120. Optionally, the processor 110 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0095] The memory 120 may include random access memory (RAM) or read-only memory (ROM). The memory 120 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 120 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, etc. The data storage area may also store data created by the electronic device 100 during use.

[0096] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the distributed resource interaction control method for autonomous distribution radio areas as described above.

[0097] This embodiment provides a structural block diagram of a computer-readable storage medium, such as... Figure 10 As shown. The computer-readable storage medium 300 stores program code that can be called by a processor to execute the methods described in the above method embodiments.

[0098] The computer-readable storage medium 300 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 300 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 300 has storage space for program code 310 that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code 310 may be compressed, for example, in a suitable form.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A distributed resource interaction control method for autonomous distribution radio areas, characterized in that, The distribution radio area is equipped with interactive terminals that communicate with and correspond one-to-one with distributed resource devices. The interactive terminals interact with their corresponding distributed resource devices to obtain electrical information, and are divided into master nodes, election nodes, and follower nodes according to their governance responsibilities, including the following steps: Based on the scenario of distribution radio area resource management, a decentralized autonomous architecture is determined, and a corresponding self-organizing method is adopted to determine the master node: among which, Based on the resource management and control scenario of distribution transformer area, a decentralized autonomous architecture is determined, including: when facing reverse heavy overload scenario, a decentralized autonomous architecture with fixed master nodes is adopted; when facing voltage over-limit or integrated coordination and control of transformer area source-grid-load-storage, a decentralized autonomous architecture with dynamically selected master nodes is adopted. If a decentralized autonomous architecture with a fixed master node is adopted, the master node is selected based on the transformer area topology and / or the transformer area governance responsibility. If a decentralized autonomous architecture with dynamic master node selection is adopted, the master node is elected in real time by the election node according to the resource management scenario requirements. The master node regulates the election and follower nodes based on node decision-making indicators; among which... The node decision indicators include voltage over-limit indicators, voltage sensitivity indicators, overload risk indicators, and net output ratio indicators, which are calculated by the interactive terminal based on the electrical information it acquires. Based on the resource management scenario requirements, the election nodes elect a master node in real time. This process includes: the election nodes configuring the weights of decision indicators for each node based on the resource management scenario of the distribution area, and calculating their weighted sum as their own master node selection indicator; while broadcasting governance topics, the election nodes listen to the governance topics published by other election nodes and compare their own master node selection indicator with the listened master node selection indicator. The election node with the higher master node selection indicator is set as a proxy node to continue broadcasting governance topics, while the election node with the lower master node selection indicator stops publishing its own governance topics. This process is repeated until the election node with the highest master node selection indicator is elected as the master node.

2. The distributed resource interaction control method according to claim 1, characterized in that, When considering the integrated coordination and control scenario of power generation, grid, load and storage in a distribution area, the node decision indicators also include economic indicators, which include the sum of the net benefits of all distributed resource devices participating in the governance.

3. The distributed resource interaction control method according to claim 1, characterized in that, The voltage exceedance level index is expressed as follows: in, branch road i Middle node j In the t The degree of voltage exceeding the limit at any given time. branch road i Middle node j In the t The voltage exceeds the limit at any given time. This is the estimated minimum voltage value after exceeding the limit. This represents the estimated maximum voltage value after exceeding the limit.

4. The distributed resource interaction control method according to claim 1, characterized in that, The voltage sensitivity index is expressed as: in, For voltage sensitivity indicators, branch road i Middle node j The rated apparent capacity, and Branch roads i Middle node j The active real-time voltage sensitivity and reactive real-time voltage sensitivity.

5. The distributed resource interaction control method according to claim 1, characterized in that, The overload risk index is expressed as: in, As an overload risk indicator, and Representing branch roads i Middle node j In the t Active power and reactive power at any given time. Indicates a branch i or transformer k The rated apparent power.

6. The distributed resource interaction control method according to claim 1, characterized in that, The net output ratio is expressed as follows: in, Indicates a branch i Middle node j In the t Real-time power generation at any given moment. Indicates a branch i Middle node j In the t Real-time load at any given moment.

7. A plug-and-play interactive terminal for distributed resource autonomy in distribution radio areas, characterized in that, A method for implementing the distributed resource interaction control method as described in any one of claims 1-6, comprising: The communication interface module is connected to the distributed resource device and is used to exchange communication messages with the distributed resource device. The dynamic protocol feature library is used to store various communication protocol feature vectors of multi-brand distributed resource devices. The communication protocol feature vectors are obtained by extracting the temporal features of the messages using a temporal convolutional network. The protocol self-identification module is used to identify the protocol type and its confidence level based on the photovoltaic inverter response message using a protocol identification model built on a time-series convolutional network. When the confidence level is less than a set threshold, the protocol identification model is updated online based on the clustering of unidentified messages. The control module is used to send probe messages to the distributed resource device according to the identified protocol type, receive the response messages returned by the distributed resource device and parse them to obtain the function code and data code. The data code includes message data containing electrical information returned by the distributed resource device. The judgment module is used to determine whether the recognition was successful based on the confidence level, function code, and data code. The decentralized autonomous control module is used to determine the decentralized autonomous architecture based on the resource management scenario of the distribution area, and then adopt the corresponding self-organizing method to determine its own governance responsibilities and execute corresponding control operations to achieve plug-and-play interactive autonomy of distributed resources.

8. The plug-and-play interactive terminal according to claim 7, characterized in that, Also includes: The online operation and maintenance module is used for remote configuration and management, status self-diagnosis and reporting, and log recording; The security encryption module is used to encrypt data transmission and verify received data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the distributed resource interaction control method for distribution area autonomy as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed resource interaction control method for autonomous distribution radio areas as described in any one of claims 1-6.

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