Power supply bus monitoring method and device

Abstract

The invention provides a power supply bus monitoring method and device, and relates to the technical field of power supply bus monitoring. The method comprises the following steps: carrying out standardization processing on collected operation data to obtain local storage data in a unified format; performing real-time monitoring and state judgment on the corresponding bus system based on the local storage data, and generating capacity information of the corresponding bus system; the edge data processing servers communicate with each other through a lightweight communication protocol to share respective capacity information; generating a load transfer instruction based on a preset distributed capacity coordination rule set; and the edge data processing server sends a load transfer instruction to the bus system of the edge data processing server and the opposite bus system. According to the method, the limitations of data islands, data isomerism, communication limitation and difficulty in efficient utilization of dispersed historical data are broken through, a light-weight and high-robustness distributed coordination mechanism is constructed, and dynamic and rapid mutual assistance and distribution of cross-regional bus capacity based on a real-time state are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply bus monitoring, and in particular to a power supply bus monitoring method and device. Background Art

[0002] Multiple power bus systems are distributed across diverse geographic regions, operating independently and each equipped with its own data acquisition equipment and management platform. However, due to historical reasons or differing construction standards, these systems employ significant differences in data acquisition methods, data formats, and communication protocols, resulting in insurmountable data silos between systems. This data silo phenomenon severely hinders cross-regional data sharing and collaborative management, making it difficult to quickly obtain information on excess capacity in other areas when load pressure in one region arises.

[0003] Furthermore, some power bus systems are located in areas with underdeveloped communications infrastructure or complex geographical environments. This can lead to data transmission delays, instability, and even, in extreme cases, communication interruptions between systems. Existing centralized management or collaborative approaches that rely on stable communications struggle to operate effectively in such environments, further exacerbating the challenges of cross-regional capacity coordination.

[0004] In the actual operation of the power system, the distribution and demand of power loads are dynamic and change rapidly. The need for load transfer or bus capacity sharing may arise between bus systems in different regions at different time points. For example, when one region experiences a peak load and another region has excess capacity. This capacity sharing demand often requires decision-making and execution to be completed in a short period of time to avoid load loss or improve system operation efficiency. However, the existing independently operated system architecture and data silos make it extremely difficult to quickly determine demand based on real-time status, discover available capacity, and coordinate cross-regional capacity. Traditional methods based on fixed plans or manual coordination have slow response speeds and are difficult to adapt to rapid dynamic changes in load.

[0005] At the same time, each busbar system has accumulated a vast amount of historical load data and operating status data over its long-term operation. This data is stored in independent local systems, lacking a unified interface and format, making centralized analysis and utilization difficult. While this historical data contains information on regional load characteristics and synergy potential, its fragmented and heterogeneous nature makes it difficult to effectively integrate and guide dynamic capacity coordination strategies or optimize and adjust rules.

[0006] In summary, in power bus systems with multiple geographically dispersed locations, independent data collection and management systems, and varying data formats and communication protocols, some regions face the constraints of weak communication infrastructure. Furthermore, bus capacity sharing demands dynamically change over time and require rapid response. Furthermore, the volume of historical load data across each system is enormous and stored in distributed locations. Existing technologies lack a lightweight, highly robust distributed collaboration mechanism capable of rapid response based on real-time status. This mechanism can effectively overcome the limitations of data silos, heterogeneous data, and limited communication, and achieve dynamic and rapid cross-regional bus capacity coordination and allocation. Summary of the Invention

[0007] The purpose of the present invention is to provide a power supply bus monitoring method and device, breaking through the limitations of data silos, data heterogeneity, communication limitations, and the difficulty in efficiently utilizing distributed historical data, and to construct a lightweight and highly robust distributed collaboration mechanism to achieve dynamic and rapid mutual assistance and allocation of cross-regional bus capacity based on real-time status.

[0008] In a first aspect, the present invention provides a power supply bus monitoring method, comprising the following steps:

[0009] After an edge data processing server is deployed in each bus system, the edge data processing server collects the operating data of the corresponding bus system and performs standardization on the collected operating data to obtain local storage data in a unified format;

[0010] Based on the locally stored data, real-time monitoring and status judgment of the corresponding bus system are performed to generate capacity information of the corresponding bus system; the capacity information includes a capacity status flag, capacity demand, and available capacity;

[0011] All bus systems use their own edge data processing servers to communicate with each other through lightweight communication protocols to share their capacity information;

[0012] When each edge data processing server detects that its own bus system has a capacity demand or receives information that the other party's bus system has a capacity demand, each edge data processing server generates a load transfer instruction based on the status information sent by its own bus system and the status information sent by the other party's bus system and based on a preset distributed capacity coordination rule set; the status information includes the locally stored data and the capacity information;

[0013] The edge data processing server sends load transfer instructions to its own bus system and the other bus system, so that the bus system with surplus power resources allocates power resources to the bus system with capacity demand.

[0014] The power supply bus monitoring method provided by the present invention breaks through the data island and data heterogeneity limitations of the cross-regional power bus system, realizes the effective integration and standardization of distributed data, lays the foundation for collaboration, and improves the operating efficiency and power supply reliability of the regional power system.

[0015] Furthermore, when each edge data processing server detects that its own bus system has a capacity demand or receives information that the other party's bus system has a capacity demand, each edge data processing server performs energy-demand matching, priority determination, and load transfer amount calculation based on the status information sent by its own bus system and the status information sent by the other party's bus system, and generates a load transfer instruction based on a preset distributed capacity coordination rule set, including the following steps:

[0016] S1. The edge data processing server determines whether its bus system is in a capacity demand state according to the capacity status flag of its own bus system. If it is in a capacity demand state, step S11 is executed. Otherwise, step S12 is executed as the identity of the other bus system.

[0017] S11. The edge data processing server selects the other bus system whose available capacity is greater than the capacity requirement of its own bus system based on the available capacity of the other bus system, and forms a set of candidate other bus systems;

[0018] S12. The edge data processing server prioritizes the other bus systems in the candidate set of other bus systems according to the priority rules contained in the distributed capacity coordination rule set;

[0019] S2. The edge data processing server selects the other bus system with the highest priority as the target bus system based on the priority sorting results, and calculates the load transfer amount based on the capacity demand of its own bus system and the available capacity of the target bus system, and generates a load transfer instruction; the load transfer instruction includes the identifier of the target bus system, the load transfer amount and the load transfer time.

[0020] Furthermore, the specific steps in step S11 include:

[0021] The edge data processing server obtains the new energy access ratio of the other party's bus system. If the new energy access ratio is greater than a first preset ratio threshold, steps A1-A2 are executed; otherwise, the other party's bus system is included in the candidate set of other party's bus systems.

[0022] A1. The edge data processing server obtains the historical renewable energy power generation data of the other party's bus system and, based on this historical renewable energy power generation data, uses a preset time series prediction model to predict the fluctuation range of renewable energy power generation in the future.

[0023] A2. The edge data processing server calculates the lower limit of the confidence interval of the capacity that can be provided by the other party's bus system based on the fluctuation range of the renewable energy power generation power. If the lower limit of the confidence interval is greater than the capacity demand of its own bus system, the other party's bus system is included in the candidate other party's bus system set.

[0024] Furthermore, the specific steps in step S12 include:

[0025] The edge data processing server obtains the new energy access ratio of each candidate counterpart bus system. If the new energy access ratio is higher than the second preset ratio threshold, the edge data processing server obtains the real-time environmental information of the counterpart bus system and adjusts the priority of the counterpart bus system according to the real-time environmental information.

[0026] Furthermore, the edge data processing server obtains the new energy access ratio of each candidate counterpart bus system. If the new energy access ratio is higher than a second preset ratio threshold, the step of obtaining real-time environmental information of the counterpart bus system and adjusting the priority of the counterpart bus system according to the real-time environmental information includes:

[0027] The edge data processing server obtains the new energy access ratio of each candidate counterpart bus system. If the new energy access ratio is higher than the second preset ratio threshold, step B1 is executed; otherwise, the systems are sorted according to the original priority.

[0028] B1. The edge data processing server queries the renewable energy type. If the renewable energy type includes a photovoltaic power generation system, step B2 is executed. If the renewable energy type includes a wind power generation system, step B3 is executed. If the renewable energy type includes both a photovoltaic power generation system and a wind power generation system, step B2 is executed first, followed by step B3.

[0029] B2. The edge data processing server obtains the real-time light intensity at the location of the photovoltaic power generation system and predicts the photovoltaic power generation power based on the real-time light intensity. If the photovoltaic power generation power is lower than a first preset power threshold, the priority of the other bus system is lowered; otherwise, the priority of the other bus system remains unchanged.

[0030] B3. The edge data processing server obtains the real-time wind speed at the location of the wind power generation system and predicts the wind power generation power based on the real-time wind speed. If the wind power generation power is lower than the second preset power threshold, the priority of the other bus system is lowered; otherwise, the priority of the other bus system is maintained unchanged.

[0031] Furthermore, the priority rules include:

[0032] The closer the distance between the other bus system and your own bus system is, the higher the priority.

[0033] The more times the other bus system has provided mutual assistance to its own bus system, the higher its priority.

[0034] The lower the importance level of the other party's bus system, the higher the priority.

[0035] Furthermore, the specific steps in step S12 include:

[0036] The edge data processing server obtains the load type of each candidate counterpart bus system; the load type includes a common type and an important type;

[0037] The priority of the other party's bus system is adjusted according to the proportion of important types in the load types.

[0038] In a second aspect, the present invention provides a power supply bus monitoring device, comprising:

[0039] A data processing module is used to collect operating data of the corresponding bus system through the edge data processing server after the edge data processing server is deployed in each bus system, and to standardize the collected operating data to obtain local storage data in a unified format;

[0040] A first generating module is configured to perform real-time monitoring and status determination of a corresponding bus system based on the locally stored data, and generate capacity information of the corresponding bus system; the capacity information includes a capacity status flag, a required capacity, and an available capacity;

[0041] Communication module, used for all bus systems to communicate with each other through their respective edge data processing servers via lightweight communication protocols to share their respective capacity information;

[0042] a second generation module configured to generate a load transfer instruction based on a preset distributed capacity coordination rule set, when each edge data processing server detects a capacity demand on its own bus system or receives information about a capacity demand on the other party's bus system, based on status information sent by its own bus system and status information sent by the other party's bus system; the status information includes the locally stored data and the capacity information;

[0043] The allocation module is used for the edge data processing server to send load transfer instructions to its own bus system and the other party's bus system, so that the bus system with surplus power resources allocates power resources to the bus system with capacity demand.

[0044] Furthermore, when each edge data processing server detects that its own bus system has a capacity demand or receives information that the other party's bus system has a capacity demand, the second generation module is used to generate a load transfer instruction based on the status information sent by its own bus system and the status information sent by the other party's bus system and based on a preset distributed capacity coordination rule set:

[0045] S1. The edge data processing server determines whether its bus system is in a capacity demand state according to the capacity status flag of its own bus system. If it is in a capacity demand state, step S11 is executed. Otherwise, step S12 is executed as the identity of the other bus system.

[0046] S11. The edge data processing server selects the other bus system whose available capacity is greater than the capacity requirement of its own bus system based on the available capacity of the other bus system, and forms a set of candidate other bus systems;

[0047] S12. The edge data processing server prioritizes the other bus systems in the candidate set of other bus systems according to the priority rules contained in the distributed capacity coordination rule set;

[0048] S2. The edge data processing server selects the other bus system with the highest priority as the target bus system based on the priority sorting results, and calculates the load transfer amount based on the capacity demand of its own bus system and the available capacity of the target bus system, and generates a load transfer instruction; the load transfer instruction includes the identifier of the target bus system, the load transfer amount and the load transfer time.

[0049] Furthermore, the second generation module is executed when the edge data processing server screens out, based on the available capacity of the other bus system, a bus system having an available capacity greater than the required capacity of its own bus system and forms a set of candidate bus systems:

[0050] The edge data processing server obtains the new energy access ratio of the other party's bus system. If the new energy access ratio is greater than a first preset ratio threshold, steps A1-A2 are executed; otherwise, the other party's bus system is included in the candidate set of other party's bus systems.

[0051] A1. The edge data processing server obtains the historical renewable energy power generation data of the other party's bus system and, based on this historical renewable energy power generation data, uses a preset time series prediction model to predict the fluctuation range of renewable energy power generation in the future.

[0052] A2. The edge data processing server calculates the lower limit of the confidence interval of the capacity that can be provided by the other party's bus system based on the fluctuation range of the renewable energy power generation power. If the lower limit of the confidence interval is greater than the capacity demand of its own bus system, the other party's bus system is included in the candidate other party's bus system set.

[0053] From the above, it can be seen that the power supply bus monitoring method provided by the present invention is intended to solve the problem of how to achieve dynamic and rapid mutual assistance and distribution of cross-regional capacity for multiple power bus systems that are geographically dispersed, data heterogeneous, communication-restricted, and historical data-dispersed. Its core working principle is to build an intelligent network based on edge data processing servers and distributed collaboration rules. By performing data processing, status judgment, and distributed collaboration based on preset rules on the edge side, combined with lightweight communication, it effectively overcomes challenges such as data silos, heterogeneity, communication restrictions, and historical data dispersion, and realizes dynamic and rapid mutual assistance and distribution of cross-regional bus capacity.

[0054] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of a power supply bus monitoring method provided in an embodiment of the present invention.

[0056] Figure 2 A schematic structural diagram of a power supply bus monitoring device provided in an embodiment of the present invention.

[0057] Description of labels:

[0058] 100, data processing module; 200, first generation module; 300, communication module; 400, second generation module; 500, allocation module. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0060] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0061] Reference Attachment Figure 1 The present invention provides a power supply bus monitoring method, comprising the following steps:

[0062] After deploying an edge data processing server in each bus system, the edge data processing server collects the operating data of the corresponding bus system and standardizes the collected operating data to obtain local storage data in a unified format. The operating data includes real-time and historical operating data of the bus system.

[0063] Based on the locally stored data, the corresponding bus system is monitored and its status judged in real time, generating the capacity information of the corresponding bus system; the capacity information includes the capacity status flag, capacity demand and available capacity;

[0064] All bus systems use their respective edge data processing servers to communicate with each other through a lightweight communication protocol to share their respective capacity information, or all bus systems use their respective edge data processing servers to communicate with the central data processing server through a lightweight communication protocol to share their respective capacity information;

[0065] When each edge data processing server detects that its own bus system has capacity demand or receives information about capacity demand from the other party's bus system, it performs energy-demand matching, priority determination, and load transfer calculation based on the status information sent by its own bus system and the status information sent by the other party's bus system, and generates a load transfer instruction based on a preset set of distributed capacity coordination rules; the status information includes locally stored data and capacity information;

[0066] The edge data processing server sends load transfer instructions to its own bus system and the other bus system, so that the bus system with surplus power resources allocates power resources to the bus system with capacity demand, realizing dynamic mutual assistance and allocation of cross-regional bus capacity.

[0067] Edge data processing servers are deployed in each bus system. These servers connect to local data collection equipment and collect bus system operational data, including instantaneous and historical data. The collected data undergoes standardization, converting data in different formats into a unified format and storing it in the edge server's local storage. This enables interoperability of data from different bus systems. Based on this locally stored standardized data, the edge server monitors the bus system in real time and determines its status, generating capacity information including capacity status, demand, and available capacity. This provides the foundational status data required for capacity coordination. Edge servers communicate with each other and with the central server via a lightweight communication protocol to share capacity information. This lightweight protocol adapts to communication-constrained environments and improves system robustness. Shared capacity information enables each system to understand the capacity status of other systems, providing information for subsequent coordination. When a capacity demand is detected or received, the edge server utilizes its own and other edge server status information to perform energy-demand matching, prioritize, and calculate load shifting based on a pre-defined distributed rule set. This distributed rule set allows each edge server to make decisions autonomously or collaboratively, improving response speed. The use of status information ensures that decisions are based on real-time conditions. Energy-demand matching identifies supply and demand relationships, priority determination determines the order of mutual assistance, and load transfer calculation determines the specific transfer amount. Finally, the edge server generates and sends load transfer instructions to the relevant bus systems. Once the instructions are executed, power resources are transferred from the surplus system to the demand system, achieving cross-regional capacity mutual assistance.

[0068] Specifically, the method proposed in this application addresses the data silos, limited communication, and slow response to dynamic capacity demands faced by cross-regional power bus systems by deploying edge data processing servers within each bus system. First, the edge servers collect operational data from each bus system and, through standardization, convert the heterogeneous data into a unified format for local storage, breaking down data silos between systems and enabling data interoperability. Next, based on this local standardized data, the edge servers monitor and assess the bus system's status in real time, generating capacity information including capacity status, demand, and availability. This information reflects the bus system's current operating status and capacity potential. All edge servers communicate with each other and with the central server using a lightweight communication protocol to share their capacity information. This lightweight protocol consumes little bandwidth and improves system reliability in areas with weak communication infrastructure. When any edge server detects a capacity demand within its own system or receives demand information from another system, it makes a decision based on its own and the other system's status information and a pre-set set of distributed capacity coordination rules. This rule set guides the server in matching energy and demand, identifying viable mutual assistance partners, prioritizing and selecting the optimal mutual assistance source, and calculating the specific load transfer amount. This distributed decision-making mechanism enables the system to quickly respond to dynamically changing load demands, overcoming the slow response times of traditional centralized decision-making. Finally, the edge server generates a load transfer instruction and sends it to the relevant bus systems. Once executed, power resources are transferred from surplus systems to demand systems, enabling dynamic cross-regional bus capacity coordination and allocation, improving the overall operational efficiency and reliability of the power system.

[0069] In some specific embodiments, consider two adjacent bus systems, System A and System B. System A's edge data processing server detects an increase in load on System A and determines that there is a demand for 10 megawatts of capacity. System B's edge data processing server, through local monitoring, determines that System B has 15 megawatts of available capacity. System A's edge server sends its capacity information to System B's edge server via the MQTT protocol. System B's edge server receives System A's demand information. The edge servers of Systems A and B collaborate according to a preset set of distributed capacity coordination rules. The rule set includes priority rules based on geographic distance and historical mutual assistance records. System B's edge server determines that it can meet System A's demand and, based on the rules, determines that System A has a higher mutual assistance priority. System B's edge server calculates the amount of load that can be transferred to System A, for example, 10 megawatts. System B's edge server generates a load transfer instruction containing the target system identifier, System A, the load transfer amount (10 megawatts), and the transfer time. The instruction is sent via the MQTT protocol to System A's edge server and System B's local control system. After receiving the instruction, System A's local control system adjusts its load access strategy. After receiving the command, System B's local control system adjusts its power generation or load distribution strategy, preparing to transfer 10 MW of load to System A. Specifically, upon receiving the command, System B's local control system performs specific operations (for example, adjusting generator output or changing transformer tap positions) to ensure power flows from System B to System A, thereby meeting System A's capacity requirements. This achieves capacity mutual assistance from System B to System A.

[0070] It's important to note that the optional "central data processing server" acts as a "macro-status aggregator" and "policy configuration issuer." It doesn't perform real-time capacity transfer calculations; it only receives key status aggregates from edge data processing servers. Based on this information, it assists with macro-policy adjustments or distributes preset rule set parameters to edge data processing servers. It also receives on-demand historical data snippets or analysis results from edge data processing servers, enabling experts to optimize the distributed capacity coordination rule set.

[0071] Finally, to address the difficulty of utilizing dispersed historical data, edge data processing servers locally store historical data for a certain period of time and perform local analysis. This historical data is primarily used to assist in local status judgment (for example, determining whether the current load is abnormally high based on historical load curves) or to query other edge data processing servers for specific historical data fragments or their local analysis results as needed. This supports manual configuration or periodic optimization and adjustment of the distributed capacity coordination rule set, thereby efficiently utilizing dispersed historical data and guiding rule setting.

[0072] When communication is limited, the edge data processing server can operate independently or conduct limited proximal collaboration with a few communicative edge data processing servers based on local status judgment and preset "independent operation rules" or "proximal collaboration rules". For example, if communication with a neighbor is interrupted, the rule set will automatically exclude the option of capacity mutual assistance through that neighbor. The use of historical data is achieved through the local analysis capabilities of the edge data processing server to assist local status judgment (such as judging the relative position of the current load level based on the historical load curve) or to query other edge data processing servers for specific historical data fragments or their local analysis results (such as the historical load rate of a line) as needed to support the configuration or manual adjustment of the rule set and avoid large-scale raw data transmission.

[0073] In certain embodiments, when each edge data processing server detects that its own bus system has a capacity demand or receives information that the other party's bus system has a capacity demand, each edge data processing server performs energy-demand matching, priority determination, and load transfer amount calculation based on a preset distributed capacity coordination rule set according to the status information sent by its own bus system and the status information sent by the other party's bus system, and generates a load transfer instruction, including the following steps:

[0074] S1. The edge data processing server determines whether its bus system is in a capacity demand state according to the capacity status flag of its own bus system. If it is in a capacity demand state, step S11 is executed. Otherwise, step S12 is executed as the identity of the other bus system.

[0075] S11. The edge data processing server selects the other bus system whose available capacity is greater than the capacity requirement of its own bus system based on the available capacity of the other bus system, and forms a set of candidate other bus systems;

[0076] S12. The edge data processing server prioritizes the other bus systems in the candidate other bus system set according to the priority rules contained in the distributed capacity coordination rule set;

[0077] S2. The edge data processing server selects the other bus system with the highest priority as the target bus system based on the priority sorting results, and calculates the load transfer amount based on the capacity demand of its own bus system and the available capacity of the target bus system, and generates a load transfer instruction; the load transfer instruction includes the identifier of the target bus system, the load transfer amount and the load transfer time.

[0078] Specifically, when any edge data processing server detects insufficient capacity in its own bus system or receives capacity demand information from another edge data processing server, the capacity coordination process is triggered. First, the edge server determines whether it is a demander in step S1. If it is, the process proceeds to step S11. In step S11, the server compares its capacity demand with the shared capacity information of its partner bus systems, particularly the available capacity. Partner systems with available capacity less than or equal to its own demand are excluded, while partner systems with available capacity greater than its own demand are included in a candidate set. This preliminarily screens potential mutual aid partners that can meet its needs. If the server is not a demander (i.e., a potential provider), the process proceeds to step S12. In step S12, both the demander's ranking of candidate providers and the provider's ranking of potential demanders are based on the priority rules in the pre-set distributed capacity coordination rule set. These rules may consider a variety of factors to prioritize and rank candidate partners. After the priority sorting is completed, in step S2, the demander (or provider) selects the other bus system with the highest priority from the sorting results as the target of this capacity mutual assistance. Subsequently, the specific load transfer amount is calculated based on its own demand and the available amount of the target system (or vice versa). The calculation result determines the amount of power resources that need to be transferred. Finally, an instruction is generated containing the target system identifier, the calculated load transfer amount, and the predetermined load transfer time. The instruction is then sent to the relevant bus system to guide the actual transfer of power resources. Through this series of steps, the edge data processing server can autonomously complete the entire process from discovering demand to determining the mutual assistance object, calculating the transfer amount, and generating instructions in a distributed environment, realizing dynamic and rapid coordination and allocation of cross-regional bus capacity, and overcoming the problems of data silos and slow response of centralized decision-making.

[0079] In some specific embodiments, assume that edge server A and edge server B exist, connected to bus system A and bus system B, respectively. Edge server A detects that the capacity status flag of bus system A indicates a capacity demand state, with the capacity demand being 10 MW. Edge server A receives capacity information from edge server B via a lightweight communication protocol, indicating that the available capacity of bus system B is 15 MW.

[0080] Edge server A executes step S1, determines that it is in a capacity demand state, and determines to execute step S11.

[0081] In step S11, edge server A compares the available capacity of bus system B (15 MW) with its own demand (10 MW). Since 15 MW is greater than 10 MW, edge server A includes bus system B in the candidate partner bus system set.

[0082] Edge server A executes step S12. Assume that the distributed capacity coordination rule set includes a priority rule based on geographic distance, and bus system B is closest to bus system A. Edge server A prioritizes bus system B in the candidate set based on this rule. Since bus system B is the only one in the set, it has the highest priority.

[0083] Edge server A executes step S2. Bus system B with the highest priority is selected as the target bus system. The load transfer amount is calculated based on its own demand of 10MW and the available amount of 15MW of target system B. The calculation result is 10MW (the smaller value of the demand and the available amount is taken). A load transfer instruction is generated, which includes the identifier of the target bus system B, the load transfer amount of 10MW, and a preset load transfer time, such as 15 minutes in the future. As a result, edge server A generates an instruction to guide bus system B to transfer 10MW of load to bus system A, realizing the decision-making process of capacity mutual assistance.

[0084] In some embodiments, the specific steps in step S11 include:

[0085] The edge data processing server obtains the new energy access ratio of the other bus system. If the new energy access ratio is greater than a first preset ratio threshold, steps A1-A2 are executed; otherwise, the other bus system is included in the candidate set of other bus systems.

[0086] A1. The edge data processing server obtains the historical renewable energy power generation data of the other party's bus system and uses a preset time series prediction model to predict the fluctuation range of renewable energy power generation in the future based on this historical renewable energy power data.

[0087] A2. The edge data processing server calculates the lower limit of the confidence interval for the available capacity of the other party's bus system based on the fluctuation range of renewable energy power generation. If the lower limit is greater than the capacity requirement of its own bus system, the other party's bus system is included in the candidate set of other party's bus systems.

[0088] This embodiment first determines whether the available capacity of the other bus system is likely to be affected by fluctuations in renewable energy. If the renewable energy access ratio is low, its capacity is considered relatively stable and can be directly included in the candidate set, simplifying the processing flow. If the renewable energy access ratio is high, its available capacity may fluctuate significantly. In this case, the solution no longer simply determines based on the current available capacity, but further executes steps A1 and A2. In step A1, the edge data processing server obtains historical renewable energy power data for the other bus system and uses a time series prediction model to predict the fluctuation range of renewable energy power over a period of time. This step quantifies the uncertainty of future renewable energy output by leveraging historical data and prediction techniques. In step A2, based on the predicted fluctuation range of renewable energy power, the lower limit of the confidence interval for the available capacity of the other bus system is calculated. This lower limit represents the minimum capacity that the other system can provide at a certain confidence level after accounting for renewable energy fluctuations. Finally, the calculated lower limit of the confidence interval is compared with the capacity demand of the own bus system. Only when this more reliable minimum available capacity exceeds the demand is the other bus system included in the candidate set.

[0089] Specifically, this technical solution aims to address how to more reliably assess the actual available capacity of a candidate bus system when screening candidate bus systems, particularly when the system is connected to a fluctuating power source. When an edge data processing server detects a capacity demand on its own bus system or receives information about a capacity demand on a competing bus system, it needs to screen for competing bus systems that can provide the required capacity. First, the edge data processing server obtains the renewable energy access ratio of the competing bus system and compares it with a first preset ratio threshold. If the ratio is not greater than the threshold, the available capacity of the competing bus system is considered relatively stable and is directly included in the candidate bus system set. If the ratio is greater than the threshold, the available capacity is considered to be potentially fluctuating and requires further evaluation. At this point, the edge data processing server obtains historical renewable energy power data for the competing bus system and, based on this historical data, uses a preset time series forecasting model, such as an ARIMA model or LSTM model, to predict the fluctuation range of renewable energy power generation over a period of time. Based on the predicted fluctuation range of renewable energy power generation, the lower limit of the confidence interval for the available capacity of the competing bus system is calculated. The lower limit of the confidence interval reflects the minimum capacity that the other bus system can stably provide at a certain confidence level after taking into account the uncertainty of new energy fluctuations. Finally, the calculated lower limit of the confidence interval is compared with the capacity demand of the own bus system. Only when the lower limit of the confidence interval is greater than the capacity demand of the own bus system, the other bus system is included in the candidate set of other bus systems. By introducing the judgment of the proportion of new energy access, historical data analysis, time series prediction and calculation of the lower limit of the confidence interval, this scheme overcomes the shortcomings of simply screening based on the current capacity value, improves the accuracy and reliability of screening candidate systems that can stably provide the required capacity, and thus enhances the effectiveness of cross-regional capacity mutual assistance.

[0090] In some specific embodiments, assume that the first preset ratio threshold is set to 20%. The edge data processing server obtains the renewable energy access ratio of the other bus system A as 30%, which is greater than 20%. At this time, the edge data processing server obtains the historical photovoltaic and wind power generation data of bus system A. Using the preset ARI MA time series prediction model, based on historical data, it is predicted that the renewable energy power generation range of bus system A within the next hour will fluctuate within ±50MW. Based on this fluctuation range, the lower limit of the 90% confidence interval of the available capacity of bus system A is calculated to be 150MW. If the capacity demand of the own bus system is 120MW, which is less than 150MW, bus system A is included in the set of candidate opposing bus systems. If the renewable energy access ratio of the other bus system B is 15%, which is not greater than 20%, bus system B is directly included in the set of candidate opposing bus systems without the need for renewable energy fluctuation prediction and confidence interval calculation.

[0091] In some embodiments, the specific steps in step S12 include:

[0092] The edge data processing server obtains the new energy access ratio of each candidate opposing bus system. If the new energy access ratio is higher than the second preset ratio threshold, the real-time environmental information of the opposing bus system is obtained, and the priority of the opposing bus system is adjusted according to the real-time environmental information.

[0093] When the edge data processing server performs priority judgment, it first obtains the new energy access ratio data of each candidate bus system to be sorted. The obtained new energy access ratio is compared with the preset second ratio threshold. For bus systems whose new energy access ratio exceeds the threshold, the edge data processing server further obtains real-time environmental information of their location, such as light intensity or wind speed data. Based on the real-time environmental information obtained, the edge data processing server calculates or evaluates the impact of the current environment on the new energy power generation capacity of the bus system. Based on this impact, the priority of the bus system is adjusted. The adjusted priority reflects the actual reliability or potential of the bus system to provide capacity under current environmental conditions.

[0094] Specifically, this technical solution identifies systems with a higher proportion of renewable energy access when prioritizing candidate opposing bus systems. For these systems, since their power generation capacity is significantly affected by environmental factors, the solution further obtains their real-time environmental information. Real-time environmental information directly reflects the actual conditions currently affecting renewable energy generation. Based on the obtained real-time environmental information, the priority of the bus system is corrected. This correction enables the priority sorting results to more accurately reflect the actual capacity available under the current environment for bus systems with a higher proportion of renewable energy access. By adjusting the priority based on the impact of the real-time environment on renewable energy generation, the power supply reliability of the candidate systems can be more accurately assessed, thereby selecting bus systems that are more suitable as load transfer targets in subsequent steps and improving the success rate of load transfer. Obtaining the proportion of renewable energy access and setting a threshold are used to identify specific systems that require real-time environmental assessment, avoiding real-time environmental assessments for all candidate systems and reducing the processing burden. Obtaining real-time environmental information is key to obtaining data that affects renewable energy generation. Adjusting priorities based on real-time environmental information is to use real-time data to correct the initial priorities based on general rules or historical data, so that the sorting results are closer to the actual situation, solve the one-sidedness in the capacity sharing decision-making process, improve the efficiency and rationality of capacity sharing, and reduce the risks brought by fluctuations in new energy.

[0095] In some specific embodiments, when prioritizing candidate opposing bus systems, the edge data processing server obtains the real-time renewable energy access ratio of each candidate system. A second preset ratio threshold is set to 50%. For candidate systems with a renewable energy access ratio greater than 50%, the edge data processing server obtains real-time environmental information at their location. For example, if a candidate system's renewable energy type includes photovoltaics, real-time light intensity data at its location is obtained. If the real-time light intensity is lower than the light intensity corresponding to a preset first power threshold (e.g., lower than 20% of the peak light intensity on a sunny day), the priority of the candidate system is lowered. If the renewable energy type includes wind power, real-time wind speed data at its location is obtained. If the real-time wind speed is lower than the wind speed corresponding to a preset second power threshold (e.g., lower than 30% of the rated wind speed), the priority of the candidate system is lowered. If both photovoltaics and wind power are included, priority adjustment is performed taking into account the impact of light intensity and wind speed. For candidate systems with a renewable energy access ratio less than 50%, priority adjustment based on real-time environmental information is not performed, and ranking is performed according to other priority rules.

[0096] In certain embodiments, the edge data processing server obtains a new energy access ratio of each candidate counterpart bus system, and if the new energy access ratio is higher than a second preset ratio threshold, obtains real-time environmental information of the counterpart bus system, and adjusts the priority of the counterpart bus system based on the real-time environmental information, including:

[0097] The edge data processing server obtains the new energy access ratio of each candidate counterpart bus system. If the new energy access ratio is higher than the second preset ratio threshold, step B1 is executed; otherwise, the systems are sorted according to the original priority.

[0098] B1. The edge data processing server queries the renewable energy type. If the renewable energy type includes a photovoltaic power generation system, step B2 is executed. If the renewable energy type includes a wind power generation system, step B3 is executed. If the renewable energy type includes both a photovoltaic power generation system and a wind power generation system, step B2 is executed first, followed by step B3.

[0099] B2. The edge data processing server obtains the real-time light intensity at the location of the photovoltaic power generation system and predicts the photovoltaic power generation power based on the real-time light intensity. If the photovoltaic power generation power is lower than the first preset power threshold, the priority of the other bus system is lowered; otherwise, the priority of the other bus system remains unchanged;

[0100] B3. The edge data processing server obtains the real-time wind speed at the location of the wind power generation system and predicts the wind power generation power based on the real-time wind speed. If the wind power generation power is lower than the second preset power threshold, the priority of the other bus system is lowered; otherwise, the priority of the other bus system remains unchanged.

[0101] The edge data processing server first determines whether the proportion of renewable energy access in the candidate partner bus system exceeds a preset second ratio threshold. If not, the partner bus system maintains its original priority ranking. If so, the new energy type identification process begins. It identifies whether the new energy type includes a photovoltaic power generation system. If so, it obtains real-time sunlight intensity data for the location of the photovoltaic power generation system and predicts the photovoltaic power generation based on this data. The predicted photovoltaic power generation is compared with a first preset power threshold. If the predicted power is lower than the first preset power threshold, the priority of the partner bus system is lowered; otherwise, its priority is maintained. Next, it identifies whether the new energy type includes a wind power generation system. If so, it obtains real-time wind speed data for the location of the wind power generation system and predicts the wind power generation based on this data. The predicted wind power generation is compared with a second preset power threshold. If the predicted power is lower than the second preset power threshold, the priority of the partner bus system is lowered; otherwise, its priority is maintained. If the other bus system includes both photovoltaic and wind power systems, the PV and wind power systems are evaluated sequentially, and priority is adjusted based on the results. By distinguishing between renewable energy types and predicting power generation capacity based on corresponding real-time environmental data, this method can more accurately assess the actual power generation capacity of bus systems with a high proportion of renewable energy access at the current moment, thereby adjusting their priority based on actual operating conditions.

[0102] Specifically, when prioritizing candidate bus systems for distributed capacity coordination, this solution prioritizes bus systems with a higher proportion of renewable energy access. The edge data processing server obtains the renewable energy access proportion of the candidate bus systems and compares it with a second preset ratio threshold. If the renewable energy access proportion is below the threshold, the system is ranked according to established priority rules (e.g., distance, historical mutual assistance count, etc.). If the renewable energy access proportion is above the threshold, the renewable energy type of the bus system is further determined. If photovoltaic power is included, the real-time sunlight intensity is obtained and the photovoltaic power is predicted. The priority is adjusted based on the comparison between the predicted power and the first power threshold. If wind power is included, the real-time wind speed is obtained and the wind power is predicted. The priority is adjusted based on the comparison between the predicted power and the second power threshold. If both photovoltaic and wind power are included, the priority is adjusted based on a comprehensive assessment of the two. For example, if the photovoltaic power generation power falls below the threshold due to insufficient sunlight, the priority of the bus system may be lowered even if the wind power is normal, as its ability to provide stable capacity at the current moment is affected. In this way, priority judgment no longer relies solely on static information or historical data, but combines the real-time fluctuation characteristics of renewable energy power generation, which improves the accuracy of priority judgment and helps to select mutual assistance partners with more reliable current power generation capabilities during capacity coordination, thereby improving the reliability of capacity sharing.

[0103] In some specific embodiments, it is assumed that the edge data processing server A detects that there is a capacity demand for its own bus system and identifies the candidate bus system B. The edge data processing server A obtains that the new energy access ratio of bus system B is 70%, and the second preset ratio threshold is 50%. Since 70% is higher than 50%, the edge data processing server A further queries the new energy type of bus system B and finds that it includes photovoltaic power generation systems and wind power generation systems. The edge data processing server A obtains the real-time light intensity of the location of bus system B as 150W / m 2 , the first preset power threshold is 200kW. Based on 150W / m 2 Based on the light intensity, the predicted photovoltaic power generation power is 180kW. Since 180kW is lower than 200kW, the edge data processing server A determines that the priority of the bus system B should be lowered. Then, the edge data processing server A obtains the real-time wind speed of 8m / s at the location of the bus system B, and the second preset power threshold is 5m / s. Based on the wind speed of 8m / s, the predicted wind power generation power is 600kW. Since 600kW is higher than the predicted power corresponding to 5m / s (assuming it is 300kW), the wind power generation capacity is normal. Taking into account the situation of photovoltaic power generation power, the edge data processing server A finally maintained the priority of the bus system B in the candidate list unchanged (the user can set it as needed).

[0104] In some embodiments, the specific steps in step S11 include:

[0105] The edge data processing server obtains the historical load data of the other party's bus system and uses time series analysis method to predict the load fluctuation range in the future time period based on the historical load data;

[0106] Based on the load fluctuation range, the adjustment value of the capacity that can be provided by the other bus system is calculated. If the adjustment value is greater than the preset fluctuation threshold, it is determined that the other bus system does not meet the capacity mutual assistance conditions and is not included in the candidate set of other bus systems;

[0107] The capacity available of the other bus system is subtracted from the adjustment value to obtain the adjusted capacity available. If the adjusted capacity available is greater than the capacity demand of the own bus system, the other bus system is included in the candidate other bus system set.

[0108] The edge data processing server obtains the historical load data of the other party's bus system, and the historical load data is stored in the local storage data. The time series analysis method can adopt the autoregressive integral moving average model (ARIMA), long short-term memory network (LSTM) or other prediction models. Predict the load fluctuation range in the future time period, for example, the confidence interval of the predicted value can be calculated. The adjustment value of the available capacity can be calculated based on the predicted load fluctuation range, such as taking the difference between the upper limit of the fluctuation range and the predicted mean, or taking a certain percentile of the fluctuation range. The preset fluctuation threshold is an empirical value used to judge the acceptability of load fluctuations. The adjusted available capacity is the current available capacity minus the calculated adjustment value.

[0109] Specifically, when a bus system detects a capacity demand, its edge data processing server initiates a capacity mutualization process. To identify reliable power resource providers, the server needs to assess the capacity availability of other bus systems. Relying solely on the currently reported capacity availability of other systems is risky, as future load fluctuations may prevent them from consistently providing the required capacity. Therefore, this solution obtains historical load data from potential providers (the other bus system) and uses time series analysis to predict the range of load fluctuations expected over the next period of time. Based on this predicted fluctuation range, an adjustment value is calculated, reflecting the capacity that may be occupied by future load increases. If the calculated adjustment value exceeds a preset fluctuation threshold, it indicates that the other bus system's future load fluctuations are significant and its current capacity availability may be unstable. In this case, the system is deemed unsuitable for capacity provision and is excluded from the candidate set. If the adjustment value does not exceed the threshold, the calculated adjustment value is subtracted from the currently reported capacity availability of the other bus system to obtain an adjusted capacity availability. This adjusted value accounts for the uncertainty of future load increases and better represents the minimum capacity that the system can stably provide over the next period of time. Only when this adjusted available capacity exceeds the capacity demand of its own bus system is the other bus system included in the candidate bus system set. By predicting future load fluctuations and adjusting or filtering available capacity accordingly, the stability of the capacity provided by the selected candidate bus systems is improved, and the reliability of cross-regional bus capacity mutual assistance is enhanced.

[0110] In some specific embodiments, assume that bus system A has a capacity demand of 5 MW, and edge data processing server A needs to find a provider. Edge data processing server A communicates with the edge data processing server of bus system B to obtain historical load data for bus system B. Edge data processing server A uses an ARIMA model to perform time series analysis on bus system B's historical load data, predicting bus system B's load for the next hour and calculating a 95% confidence interval for the predicted load as [100 MW, 110 MW]. Bus system B currently reports a capacity availability of 15 MW. The predicted load fluctuation range is 110 MW - 100 MW = 10 MW. A preset fluctuation threshold is set at 8 MW. Since 10 MW is greater than 8 MW, bus system B is determined to have a large load fluctuation, does not meet the capacity mutual assistance condition, and is not included in the set of candidate partner bus systems. Furthermore, edge data processing server A communicates with the edge data processing server of bus system C to obtain historical load data for bus system C. Edge data processing server A uses an LSTM model to perform time series analysis on bus system C's historical load data, predicting bus system C's load for the next hour and calculating a 95% confidence interval for the predicted load as [80 MW, 85 MW]. Bus system C currently reports a capacity availability of 12 MW. The predicted load fluctuation range is 85 MW - 80 MW = 5 MW. Since 5 MW is less than 8 MW, bus system C is determined to meet the preliminary conditions. An adjustment value is calculated, for example, by taking the difference between the upper bound of the fluctuation range and the predicted mean. The predicted mean is (80 + 85) / 2 = 82.5 MW, and the adjustment value is 85 MW - 82.5 MW = 2.5 MW. The adjusted capacity is subtracted from the current available capacity of bus system C, resulting in an adjusted available capacity of 12 MW - 2.5 MW = 9.5 MW. Since 9.5 MW is greater than bus system A's capacity demand of 5 MW, bus system C is included in the candidate partner bus system set. Therefore, by considering future load fluctuations and making adjustments, more reliable capacity providers can be screened out.

[0111] In some embodiments, the step of the edge data processing server obtaining historical load data of the other party's bus system and predicting the load fluctuation range in a future time period using a time series analysis method based on the historical load data includes:

[0112] The edge data processing server obtains the historical load data of the other party's bus system and uses the time series analysis method to predict the initial load fluctuation range in the future time period based on the historical load data;

[0113] The edge data processing server queries the preset holiday and special event database to determine whether there are holidays or special events in the future time period. If so, step C1 is executed; otherwise, the initial load fluctuation range is used as the load fluctuation range in the future time period.

[0114] C1. The edge data processing server queries the corresponding load adjustment coefficient based on the type of holiday or special event, and adjusts the initial load fluctuation range based on the load adjustment coefficient to obtain the load fluctuation range in the future time period.

[0115] The edge data processing server first uses historical load data accumulated by the other bus system and applies time series analysis techniques, such as ARIMA models or LSTM networks, to predict an initial range of possible load fluctuations within a specific future time period. This initial range reflects the forecast results based on historical trends and cyclical patterns. Furthermore, the edge data processing server accesses a pre-established database that stores information on holidays and special events known to have non-periodic impacts on power load, as well as load adjustment coefficients associated with these event types. By comparing the forecast time period with the event dates in the database, the system determines whether such events exist. If not, the initial forecast range is considered sufficient to reflect future conditions. If so, the corresponding load adjustment coefficient is retrieved from the database based on the identified event type. This coefficient quantifies the expected impact of the specific event on the magnitude of load fluctuations. Finally, the edge data processing server combines the initial predicted load fluctuation range with the retrieved load adjustment coefficient to calculate a revised, more realistic load fluctuation range for the future time period. This correction mechanism improves the accuracy of load fluctuation range predictions, especially in the presence of non-periodic events.

[0116] Specifically, this technical solution aims to optimize the accuracy of predicting load fluctuation ranges in future time periods, thereby more reliably assessing the capacity availability of the other party's bus system. First, the edge data processing server receives historical load data from the other party's bus system and processes this data using time series analysis methods, such as seasonal decomposition or machine learning models, to predict a preliminary load fluctuation range. This initial range is primarily based on the cyclical and trend characteristics of the historical data. Next, the edge data processing server queries a database containing information on special events, such as holidays and major events. By checking whether the predicted future time period overlaps with any event dates in the database, the system can identify potential non-cyclical load influencing factors. If a holiday or special event is detected, the system searches the database for a preset load adjustment factor based on the event type. For example, a specific holiday may cause the load fluctuation range to expand, resulting in an adjustment factor greater than 1; while another event may cause the fluctuation range to contract, resulting in a factor less than 1. The edge data processing server then uses this adjustment factor to adjust the initially predicted load fluctuation range. For example, if the initially predicted fluctuation range is ±X MW and the adjustment factor is K, the adjusted fluctuation range becomes ±(X*K) MW. If no holidays or special events are detected in the future time period, the initially predicted load fluctuation range is used directly. This approach more comprehensively considers the impact of historical periodic patterns and non-periodic events, resulting in more accurate load fluctuation range forecasts. More accurate load fluctuation range forecasts facilitate more precise calculations of the available capacity of the other bus system in subsequent steps, thereby improving the reliability of energy-demand matching and load shifting decisions.

[0117] In some specific embodiments, it is assumed that the edge data processing server predicts that the initial load fluctuation range of the other party's bus system in the next 24 hours is ±5MW. The system queries the holiday and special event database and finds that the next 24 hours will include a special event that is known to significantly affect the load. The load adjustment coefficient corresponding to this special event type in the database is set to 1.5. The edge data processing server adjusts the initial fluctuation range according to the coefficient, and calculates the adjusted load fluctuation range to be ±(5MW*1.5)=±7.5MW. The adjusted range reflects the larger load fluctuation amplitude that may be caused by special events. By using this wider fluctuation range to evaluate the available capacity of the other party's bus system, the risk of inaccurate capacity assessment due to forecast deviation can be reduced, thereby improving the robustness of cross-regional capacity mutual assistance decision-making.

[0118] In some embodiments, priority rules include:

[0119] The closer the distance between the other bus system and your own bus system is, the higher the priority.

[0120] The more times the other bus system has provided mutual assistance to its own bus system, the higher its priority.

[0121] The lower the importance level of the other party's bus system, the higher the priority.

[0122] Priority rules are used to determine the priority of candidate bus systems. Location distance is calculated using geographic coordinates. The closer the distance, the lower the power transmission loss. Historical mutual assistance counts are calculated by recording successful mutual assistance events. A greater number of such instances indicates greater inter-system collaboration experience. Importance levels are determined based on pre-defined system classifications. Systems with lower importance levels carry less critical loads or functions. Priority is determined based on this information. This allows selection of a suitable bus system for resource allocation from among multiple bus systems capable of providing power resources.

[0123] Specifically, when the edge data processing server detects a capacity demand in its own bus system or receives information about a capacity demand in a partner bus system, it selects one candidate partner bus system with available capacity for load transfer. This selection process is based on the priority rules in the pre-set distributed capacity coordination rule set. First, the distance between each candidate partner bus system and the own bus system is determined. Systems with closer distances are assigned higher priority, thereby reducing power transmission losses and improving response speed. Second, the number of historical mutual assistance provided by each candidate partner bus system to the own bus system is determined. Systems with more mutual assistance are assigned higher priority, thereby improving the reliability of mutual assistance. Third, the importance level of each candidate partner bus system is determined. Systems with lower importance levels are assigned higher priority, thereby ensuring the stable operation of critical systems. The edge data processing server applies these priority rules to rank the candidate partner bus systems and selects the system with the highest priority as the target bus system for subsequent load transfer calculation and instruction generation.

[0124] In some specific implementations, assume that bus system A has capacity demand and identifies three candidate partner bus systems B, C, and D with available capacity. System B is 10 km away from A, has a history of five mutual assistances, and is rated low in importance. System C is 50 km away from A, has a history of ten mutual assistances, and is rated medium in importance. System D is 20 km away from A, has two history of mutual assistances, and is rated low in importance.

[0125] According to the priority rule, distance priority: B>D>C;

[0126] Priority of historical mutual assistance times: C>B>D;

[0127] Importance level priority: B=D>C.

[0128] The final priority can be determined by weighted summation, for example, weights can be set for distance, number of historical mutual assistance, and importance level respectively. Alternatively, a hierarchical sorting method can be used, for example, first sorting by importance level (low>medium>high), then sorting systems of the same level by distance (near>far), and finally sorting systems of the same distance by number of historical mutual assistance (more>less). When using hierarchical sorting, first, the importance levels of B and D are both low, higher than C (medium). Between B and D, B is closer to A (10km<20km), so B has a higher priority than D. C's importance level is medium, and its priority is lower than B and D. The final sorting result is B>D>C. The edge data processing server selects system B with the highest priority as the target bus system.

[0129] It should be noted that the importance level of the bus system is pre-set, and the importance level can be determined according to the importance of the bus system service object. For example, the bus system serving hospitals, schools, scientific research institutions, rail transit, etc. can be set to a higher importance level; when allocating resources between bus systems, this implementation reduces the priority of the bus system with a higher importance level in order to ensure the normal operation of important service objects, and gives priority to allocating power resources to the bus system with a lower importance level, thereby reducing the occupation of power resources for important service objects.

[0130] In some embodiments, the importance level can be determined based on the degree of fluctuation of the power load of the bus system service object. For example, when an industrial park starts large equipment, its power load fluctuates significantly. If the power resources of the bus system serving the industrial park are occupied, it may affect the operation of the large equipment. Therefore, the priority of the bus system can be lowered to avoid its power resources being allocated preferentially.

[0131] In some embodiments, the specific steps in step S12 include:

[0132] The edge data processing server obtains the load type of each candidate counterpart bus system; the load type includes common type and important type;

[0133] Adjust the priority of the other bus system according to the proportion of important types in the load types.

[0134] The edge data processing server obtains the load type information of the candidate bus system, which is used to identify the load characteristics served by the bus system. Load types are divided into ordinary types and important types. Important loads may include hospitals, transportation hubs, and other facilities with high requirements for power supply reliability. According to the proportion of important loads in the total load, an adjustment factor is calculated or the priority adjustment range is directly determined based on the proportion range. The priority adjustment process lowers the priority of bus systems with a high proportion of important loads, and increases or maintains the priority of bus systems with a low proportion of important loads. Therefore, when prioritizing, the importance of the load served by the bus system can be taken into consideration to avoid using bus systems with a high proportion of important loads as the priority power resource provider.

[0135] Specifically, this technical solution addresses the issue of not considering the importance of the loads served by a bus system when prioritizing candidate partner bus systems. The edge data processing server first obtains data on the load types served by each candidate partner bus system. This load type data is categorized into common and critical types. For example, this data is obtained by querying a database of user types connected to the bus system or by classifying the loads based on user contract information. The server then calculates the proportion of critical loads to the total load in each candidate partner bus system. Based on this proportion, a preset rule or function is applied to adjust the priority ranking of the bus system. Bus systems with a higher proportion of critical loads have their priority lowered. For example, a threshold can be set such that when the proportion of critical loads exceeds the threshold, the priority is lowered by one level; or a continuous function can be used such that the higher the proportion of critical loads, the greater the decrease in priority. In this way, when selecting power resource providers, the system tends to select bus systems with a lower proportion of critical loads, thereby reducing the impact of load transfer on the power supply stability of the provider's own critical loads and improving the rationality of load transfer decisions.

[0136] In some specific embodiments, the edge data processing server obtains the load type data of the candidate bus system. For example, bus system A serves an industrial park and a hospital, the load of the industrial park is of ordinary type, and the load of the hospital is of important type. Bus system B serves residential and commercial areas, both of which are of ordinary type. The edge data processing server calculates the proportion of important loads (hospitals) of bus system A to the total load, assuming it is 30%. The important load proportion of bus system B is calculated to be 0%. According to the preset rules, the priority of bus systems with an important load proportion of more than 20% is reduced. Therefore, the priority of bus system A is reduced, while the priority of bus system B remains unchanged. When prioritizing, the priority of bus system B will be higher than that of bus system A (when other priority factors are the same), so that the system gives priority to obtaining power resources from bus system B, thereby reducing the potential impact on the power supply stability of the hospital served by bus system A.

[0137] Please refer to Figure 2 , Figure 2 In some embodiments of the present invention, a power bus monitoring device is provided. The power bus monitoring device is integrated into a back-end control device in the form of a computer program and includes:

[0138] The data processing module 100 is used to collect the operating data of the corresponding bus system through the edge data processing server after the edge data processing server is deployed in each bus system, and standardize the collected operating data to obtain local storage data in a unified format; the operating data includes real-time operating data and historical operating data of the bus system;

[0139] The first generating module 200 is used to monitor and determine the status of the corresponding bus system in real time based on the locally stored data, and generate capacity information of the corresponding bus system; the capacity information includes a capacity status flag, a required capacity, and an available capacity;

[0140] Communication module 300, used for all bus systems to communicate with each other using their respective edge data processing servers through a lightweight communication protocol to share their respective capacity information, or for all bus systems to communicate with the central data processing server using their respective edge data processing servers through a lightweight communication protocol to share their respective capacity information;

[0141] The second generation module 400 is configured to, when each edge data processing server detects a capacity demand on its own bus system or receives information about a capacity demand on the other party's bus system, perform energy-demand matching, priority determination, and load transfer amount calculation based on the status information sent by its own bus system and the status information sent by the other party's bus system, and generate a load transfer instruction based on a preset distributed capacity coordination rule set; the status information includes locally stored data and capacity information;

[0142] The allocation module 500 is used for the edge data processing server to send load transfer instructions to its own bus system and the other party's bus system, so that the bus system with surplus power resources allocates power resources to the bus system with capacity demand, thereby realizing dynamic mutual assistance and allocation of cross-regional bus capacity.

[0143] In certain embodiments, the second generation module 400 is configured to, when each edge data processing server detects that its own bus system has a capacity demand or receives information that the other party's bus system has a capacity demand, perform energy-demand matching, priority determination, and load transfer amount calculation based on the status information sent by its own bus system and the status information sent by the other party's bus system, and generate a load transfer instruction based on a preset distributed capacity coordination rule set, execute the following:

[0144] S1. The edge data processing server determines whether its bus system is in a capacity demand state according to the capacity status flag of its own bus system. If it is in a capacity demand state, step S11 is executed. Otherwise, step S12 is executed as the identity of the other bus system.

[0145] S11. The edge data processing server selects the other bus system whose available capacity is greater than the capacity requirement of its own bus system based on the available capacity of the other bus system, and forms a set of candidate other bus systems;

[0146] S12. The edge data processing server prioritizes the other bus systems in the candidate other bus system set according to the priority rules contained in the distributed capacity coordination rule set;

[0147] S2. The edge data processing server selects the other bus system with the highest priority as the target bus system based on the priority sorting results, and calculates the load transfer amount based on the capacity demand of its own bus system and the available capacity of the target bus system, and generates a load transfer instruction; the load transfer instruction includes the identifier of the target bus system, the load transfer amount and the load transfer time.

[0148] In some embodiments, the second generating module 400 is executed when the edge data processing server selects, based on the available capacity of the other bus system, a bus system having an available capacity greater than the required capacity of its own bus system and forms a candidate bus system set:

[0149] The edge data processing server obtains the new energy access ratio of the other bus system. If the new energy access ratio is greater than a first preset ratio threshold, steps A1-A2 are executed; otherwise, the other bus system is included in the candidate set of other bus systems.

[0150] A1. The edge data processing server obtains the historical renewable energy power generation data of the other party's bus system and uses a preset time series prediction model to predict the fluctuation range of renewable energy power generation in the future based on this historical renewable energy power data.

[0151] A2. The edge data processing server calculates the lower limit of the confidence interval for the available capacity of the other party's bus system based on the fluctuation range of renewable energy power generation. If the lower limit is greater than the capacity requirement of its own bus system, the other party's bus system is included in the candidate set of other party's bus systems.

[0152] In some embodiments, the second generating module 400 is executed when the edge data processing server prioritizes the counterpart bus systems in the candidate counterpart bus system set according to the priority rules included in the distributed capacity coordination rule set:

[0153] The edge data processing server obtains the new energy access ratio of each candidate opposing bus system. If the new energy access ratio is higher than the second preset ratio threshold, the real-time environmental information of the opposing bus system is obtained, and the priority of the opposing bus system is adjusted according to the real-time environmental information.

[0154] In some embodiments, the second generation module 400 is executed when the edge data processing server obtains the new energy access ratio of each candidate counterpart bus system, obtains the real-time environmental information of the counterpart bus system if the new energy access ratio is higher than a second preset ratio threshold, and adjusts the priority of the counterpart bus system according to the real-time environmental information:

[0155] The edge data processing server obtains the new energy access ratio of each candidate counterpart bus system. If the new energy access ratio is higher than the second preset ratio threshold, step B1 is executed; otherwise, the systems are sorted according to the original priority.

[0156] B1. The edge data processing server queries the renewable energy type. If the renewable energy type includes a photovoltaic power generation system, step B2 is executed. If the renewable energy type includes a wind power generation system, step B3 is executed. If the renewable energy type includes both a photovoltaic power generation system and a wind power generation system, step B2 is executed first, followed by step B3.

[0157] B2. The edge data processing server obtains the real-time light intensity at the location of the photovoltaic power generation system and predicts the photovoltaic power generation power based on the real-time light intensity. If the photovoltaic power generation power is lower than the first preset power threshold, the priority of the other bus system is lowered; otherwise, the priority of the other bus system remains unchanged;

[0158] B3. The edge data processing server obtains the real-time wind speed at the location of the wind power generation system and predicts the wind power generation power based on the real-time wind speed. If the wind power generation power is lower than the second preset power threshold, the priority of the other bus system is lowered; otherwise, the priority of the other bus system remains unchanged.

[0159] In some embodiments, the second generating module 400 is executed when the edge data processing server selects, based on the available capacity of the other bus system, a bus system having an available capacity greater than the required capacity of its own bus system and forms a candidate bus system set:

[0160] The edge data processing server obtains the historical load data of the other party's bus system and uses time series analysis method to predict the load fluctuation range in the future time period based on the historical load data;

[0161] Based on the load fluctuation range, the adjustment value of the capacity that can be provided by the other bus system is calculated. If the adjustment value is greater than the preset fluctuation threshold, it is determined that the other bus system does not meet the capacity mutual assistance conditions and is not included in the candidate set of other bus systems;

[0162] The capacity available of the other bus system is subtracted from the adjustment value to obtain the adjusted capacity available. If the adjusted capacity available is greater than the capacity demand of the own bus system, the other bus system is included in the candidate other bus system set.

[0163] In some embodiments, the second generation module 400 is executed when the edge data processing server obtains historical load data of the other party's bus system and uses a time series analysis method to predict the load fluctuation range in a future time period based on the historical load data:

[0164] The edge data processing server obtains the historical load data of the other party's bus system and uses the time series analysis method to predict the initial load fluctuation range in the future time period based on the historical load data;

[0165] The edge data processing server queries the preset holiday and special event database to determine whether there are holidays or special events in the future time period. If so, step C1 is executed; otherwise, the initial load fluctuation range is used as the load fluctuation range in the future time period.

[0166] C1. The edge data processing server queries the corresponding load adjustment coefficient based on the type of holiday or special event, and adjusts the initial load fluctuation range based on the load adjustment coefficient to obtain the load fluctuation range in the future time period.

[0167] In some embodiments, the second generating module 400 is executed when the edge data processing server prioritizes the counterpart bus systems in the candidate counterpart bus system set according to the priority rules included in the distributed capacity coordination rule set:

[0168] The edge data processing server obtains the load type of each candidate counterpart bus system; the load type includes common type and important type;

[0169] Adjust the priority of the other bus system according to the proportion of important types in the load types.

[0170] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0171] The foregoing description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A power supply bus monitoring method, characterized in that: The following steps are involved: After an edge data processing server is deployed in each bus system, the edge data processing server collects the operating data of the corresponding bus system and performs standardization on the collected operating data to obtain local storage data in a unified format; Based on the locally stored data, real-time monitoring and status judgment of the corresponding bus system are performed to generate capacity information of the corresponding bus system; the capacity information includes a capacity status flag, capacity demand, and available capacity; All bus systems use their own edge data processing servers to communicate with each other through lightweight communication protocols to share their capacity information; When each edge data processing server detects that its own bus system has a capacity demand or receives information that the other party's bus system has a capacity demand, each edge data processing server generates a load transfer instruction based on the status information sent by its own bus system and the status information sent by the other party's bus system and based on a preset distributed capacity coordination rule set; the status information includes the locally stored data and the capacity information; The edge data processing server sends load transfer instructions to its own bus system and the other bus system, so that the bus system with surplus power resources allocates power resources to the bus system with capacity demand.

2. The power supply bus monitoring method according to claim 1, characterized in that: When each edge data processing server detects that its own bus system has a capacity demand or receives information that the other party's bus system has a capacity demand, each edge data processing server performs energy-demand matching, priority determination, and load transfer amount calculation based on the status information sent by its own bus system and the status information sent by the other party's bus system, and generates a load transfer instruction based on a preset distributed capacity coordination rule set. The steps include: S1. The edge data processing server determines whether its bus system is in a capacity demand state according to the capacity status flag of its own bus system. If it is in a capacity demand state, step S11 is executed. Otherwise, step S12 is executed as the identity of the other bus system. S11. The edge data processing server selects the other bus system whose available capacity is greater than the capacity requirement of its own bus system based on the available capacity of the other bus system, and forms a set of candidate other bus systems; S12. The edge data processing server prioritizes the other bus systems in the candidate set of other bus systems according to the priority rules contained in the distributed capacity coordination rule set; S2. The edge data processing server selects the other bus system with the highest priority as the target bus system based on the priority sorting results, and calculates the load transfer amount based on the capacity demand of its own bus system and the available capacity of the target bus system, and generates a load transfer instruction; the load transfer instruction includes the identifier of the target bus system, the load transfer amount and the load transfer time.

3. The power supply bus monitoring method according to claim 2, characterized in that: The specific steps in step S11 include: The edge data processing server obtains the new energy access ratio of the other party's bus system. If the new energy access ratio is greater than a first preset ratio threshold, steps A1-A2 are executed; otherwise, the other party's bus system is included in the candidate set of other party's bus systems. A1. The edge data processing server obtains the historical renewable energy power generation data of the other party's bus system and, based on this historical renewable energy power generation data, uses a preset time series prediction model to predict the fluctuation range of renewable energy power generation in the future. A2. The edge data processing server calculates the lower limit of the confidence interval of the capacity that can be provided by the other party's bus system based on the fluctuation range of the renewable energy power generation power. If the lower limit of the confidence interval is greater than the capacity demand of its own bus system, the other party's bus system is included in the candidate other party's bus system set.

4. The power supply bus monitoring method according to claim 3, characterized in that: The specific steps in step S12 include: The edge data processing server obtains the new energy access ratio of each candidate counterpart bus system. If the new energy access ratio is higher than the second preset ratio threshold, the edge data processing server obtains the real-time environmental information of the counterpart bus system and adjusts the priority of the counterpart bus system according to the real-time environmental information.

5. The power supply bus monitoring method according to claim 4, characterized in that: The edge data processing server obtains the new energy access ratio of each candidate counterpart bus system, and if the new energy access ratio is higher than a second preset ratio threshold, obtains real-time environmental information of the counterpart bus system, and adjusts the priority of the counterpart bus system according to the real-time environmental information, including the following steps: The edge data processing server obtains the new energy access ratio of each candidate counterpart bus system. If the new energy access ratio is higher than the second preset ratio threshold, step B1 is executed; otherwise, the systems are sorted according to the original priority. B1. The edge data processing server queries the renewable energy type. If the renewable energy type includes a photovoltaic power generation system, step B2 is executed. If the renewable energy type includes a wind power generation system, step B3 is executed. If the renewable energy type includes both a photovoltaic power generation system and a wind power generation system, step B2 is executed first, followed by step B3. B2. The edge data processing server obtains the real-time light intensity at the location of the photovoltaic power generation system and predicts the photovoltaic power generation power based on the real-time light intensity. If the photovoltaic power generation power is lower than a first preset power threshold, the priority of the other bus system is lowered; otherwise, the priority of the other bus system remains unchanged. B3. The edge data processing server obtains the real-time wind speed at the location of the wind power generation system and predicts the wind power generation power based on the real-time wind speed. If the wind power generation power is lower than the second preset power threshold, the priority of the other bus system is lowered; otherwise, the priority of the other bus system is maintained unchanged.

6. The power supply bus monitoring method according to claim 2, characterized in that: The priority rules include: The closer the distance between the other bus system and your own bus system is, the higher the priority. The more times the other bus system has provided mutual assistance to its own bus system, the higher its priority. The lower the importance level of the other party's bus system, the higher the priority.

7. The power supply bus monitoring method according to claim 2, characterized in that: The specific steps in step S12 include: The edge data processing server obtains the load type of each candidate counterpart bus system; the load type includes a common type and an important type; The priority of the other party's bus system is adjusted according to the proportion of important types in the load types.

8. A power supply bus monitoring device, characterized in that: include: A data processing module is used to collect operating data of the corresponding bus system through the edge data processing server after the edge data processing server is deployed in each bus system, and to standardize the collected operating data to obtain local storage data in a unified format; A first generating module is configured to perform real-time monitoring and status determination of a corresponding bus system based on the locally stored data, and generate capacity information of the corresponding bus system; the capacity information includes a capacity status flag, a required capacity, and an available capacity; Communication module, used for all bus systems to communicate with each other through their respective edge data processing servers via lightweight communication protocols to share their respective capacity information; a second generation module configured to generate a load transfer instruction based on a preset distributed capacity coordination rule set, when each edge data processing server detects a capacity demand on its own bus system or receives information about a capacity demand on the other party's bus system, according to status information sent by its own bus system and status information sent by the other party's bus system; the status information includes the locally stored data and the capacity information; The allocation module is used for the edge data processing server to send load transfer instructions to its own bus system and the other party's bus system, so that the bus system with surplus power resources allocates power resources to the bus system with capacity demand.

9. The power supply bus monitoring device according to claim 8, characterized in that: The second generation module is used to generate a load transfer instruction based on the status information sent by its own bus system and the status information sent by the other party's bus system, based on a preset distributed capacity coordination rule set, when each edge data processing server detects that its own bus system has a capacity demand or receives information that the other party's bus system has a capacity demand. S1. The edge data processing server determines whether its bus system is in a capacity demand state according to the capacity status flag of its own bus system. If it is in a capacity demand state, step S11 is executed. Otherwise, step S12 is executed as the identity of the other bus system. S11. The edge data processing server selects the other bus system whose available capacity is greater than the capacity requirement of its own bus system based on the available capacity of the other bus system, and forms a set of candidate other bus systems; S12. The edge data processing server prioritizes the other bus systems in the candidate set of other bus systems according to the priority rules contained in the distributed capacity coordination rule set; S2. The edge data processing server selects the other bus system with the highest priority as the target bus system based on the priority sorting results, and calculates the load transfer amount based on the capacity demand of its own bus system and the available capacity of the target bus system, and generates a load transfer instruction; the load transfer instruction includes the identifier of the target bus system, the load transfer amount and the load transfer time.

10. The power supply bus monitoring device according to claim 9, characterized in that: The second generation module is executed when the edge data processing server selects, based on the available capacity of the other bus system, the other bus system whose available capacity is greater than the capacity demand of its own bus system and forms a set of candidate other bus systems: The edge data processing server obtains the new energy access ratio of the other party's bus system. If the new energy access ratio is greater than a first preset ratio threshold, steps A1-A2 are executed; otherwise, the other party's bus system is included in the candidate set of other party's bus systems. A1. The edge data processing server obtains the historical renewable energy power generation data of the other party's bus system and, based on this historical renewable energy power generation data, uses a preset time series prediction model to predict the fluctuation range of renewable energy power generation in the future. A2. The edge data processing server calculates the lower limit of the confidence interval of the capacity that can be provided by the other party's bus system based on the fluctuation range of the renewable energy power generation power. If the lower limit of the confidence interval is greater than the capacity demand of its own bus system, the other party's bus system is included in the candidate other party's bus system set.

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