Layered collaborative optimization method and system for power distribution network

Abstract

The invention discloses a hierarchical collaborative optimization method and system for a power distribution network, and relates to the technical field of hierarchical collaboration of the power distribution network, and the method specifically comprises the following steps: building a hierarchical structure of the power distribution network when determining that a load demand priority conflict condition exists, real-time monitoring and analysis are carried out on actual power supply information of each load unit in each layer, and the layer with a resource scheduling imbalance phenomenon is screened out and is calibrated as a conflict analysis layer; obtaining load conflict information of each conflict analysis layer, analyzing the load conflict information, evaluating the conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs, and classifying the conflict degree; and respectively executing corresponding load scheduling collaborative optimization measures according to the classification result of each conflict analysis layer. According to the method, the problems of load priority conflict and resource scheduling imbalance in the high-load period of the power distribution network are solved, and the load scheduling accuracy and the system operation stability are improved.

Description

Technical Field

[0001] This invention relates to the field of hierarchical coordination technology in power distribution networks, and specifically to a hierarchical coordination optimization method and system for power distribution networks. Background Technology

[0002] With the large-scale integration of renewable energy sources (such as photovoltaic and wind power), the widespread adoption of electric vehicles, and the continuous growth in electricity demand, the operating environment of power distribution networks is becoming increasingly complex. Not only is the scale of distribution networks constantly expanding, with numerous nodes and wide geographical distribution, but the operational status of distribution networks also exhibits significant dynamic changes due to the volatility of distributed power sources and user loads. Furthermore, the types of entities involved in distribution networks are diverse, including power generation companies, users, and grid operators, each with diversified objectives and needs during operation and dispatch. Simultaneously, with the development of smart devices and monitoring technologies, the amount of data generated by distribution networks is increasing dramatically, placing higher demands on the processing capabilities of communication networks and computing platforms. As the scale of distribution networks continues to expand and their operating environment becomes increasingly complex, traditional centralized optimization methods face enormous pressure when dealing with large-scale nodes, rapid dynamic changes, and multi-entity coordination problems. Different regions and levels within the distribution network exhibit different operating characteristics and management needs, making it difficult for single-level optimization methods to simultaneously consider both local response efficiency and overall system performance. At the same time, due to frequent fluctuations in distributed power sources and load states, the requirements for real-time performance, flexibility, and stability in distribution networks are constantly increasing. Through hierarchical management, targeted optimizations can be carried out based on the characteristics of each level, improving the system's response speed and local autonomy. Furthermore, through collaboration between different levels, the overall coordination and optimization effect of the system can be strengthened, thereby better adapting to the complex, variable, and multi-entity operating environment of the power distribution network.

[0003] Existing hierarchical collaborative optimization technologies for distribution networks typically employ logical division of the distribution network according to different voltage levels, geographical regions, or functional modules, forming a multi-layered structure consisting of a main network, sub-networks, and end-user networks. Coordination mechanisms between layers enable bidirectional information transmission and the issuance of optimization commands. Specifically, the upper layer is primarily responsible for global optimization scheduling and strategy formulation, including load forecasting, power flow optimization, and resource allocation decisions; the middle layer focuses on regional coordination and local optimization, handling local power management, node regulation, and status assessment; and the lower layer focuses on equipment-level control and execution, such as output regulation of distributed generation units, energy storage system management, and end-load response. Each layer uses a communication network to achieve real-time uploading of status information and timely issuance of control commands, forming a multi-layered, collaborative, and clearly defined optimization control system to address the multi-scale dynamic changes and multi-objective management needs in distribution network operation.

[0004] The existing technology has the following shortcomings: During peak load periods in the distribution network, especially when the grid load reaches its peak, the load demand of multiple users increases simultaneously, particularly when the demand from residential, commercial, and industrial users is high, intensifying competition for electricity resources. In this situation, due to differences in the priority of electricity demand among different users, the distribution network faces multiple load demand priority conflicts. This priority conflict leads to the system failing to fully consider the priorities of various loads during resource allocation, resulting in resource scheduling imbalances. A hierarchical optimization system for the distribution network needs to allocate electricity resources rationally based on users' electricity demand and priorities. However, existing hierarchical collaborative optimization technologies for distribution networks cannot accurately optimize the scheduling of loads with different priorities based on the degree of conflict when multiple load demand priorities lead to resource scheduling imbalances. Because of this problem, the system fails to accurately allocate appropriate electricity resources to each user during peak periods, resulting in some loads not being met in time, while other loads consume excessive resources. This scheduling imbalance further leads to grid load instability, potentially causing insufficient power supply for some users, or even triggering grid protection mechanisms and causing power outages. Simultaneously, excessive power consumption by low-priority users wastes electricity resources, further reducing the operating efficiency of the distribution network, increasing the system burden, and affecting the reliability and stability of the grid.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a hierarchical collaborative optimization method and system for power distribution networks to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a hierarchical collaborative optimization method for power distribution networks, specifically comprising the following steps: During periods of high load in the distribution network, real-time monitoring of the electricity demand data and corresponding priority information of each load unit is conducted to determine whether there are load demand priority conflicts where load units with different priorities compete for the same power resources in the same time period. When it is determined that there is a conflict in load demand priority, a hierarchical structure of the distribution network is constructed, and the actual power supply information of each load unit in each layer is monitored and analyzed in real time. Layers with unbalanced resource scheduling are selected and marked as conflict analysis layers. Obtain load conflict information for each conflict analysis layer, analyze it, assess the degree of conflict in each conflict analysis layer when resource scheduling imbalance occurs, and classify them. Based on the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are implemented respectively; After implementing load dispatching coordination optimization measures, the power supply status and resource allocation changes of each load unit in each layer are monitored in real time, and the load dispatching strategy is dynamically adjusted based on the real-time monitoring results.

[0008] Preferably, when it is determined that there is a conflict in load demand priority, a hierarchical structure of the distribution network is constructed. Specifically, based on the voltage level, geographical area and physical connection relationship of the load units, all load units are divided into layers according to the preset hierarchical rules. Each layer corresponds to several load units, and the load unit identifier and real-time power demand data corresponding to each layer are recorded. Each layer is divided into load units according to voltage level and geographical area. The actual power supply information of each load unit in each layer is monitored and analyzed in real time. Layers with unbalanced resource scheduling are selected and marked as conflict analysis layers. Specifically, in each layer, the actual power supply information of the load unit is collected in real time and compared with the maximum load demand standard of the layer. The resource allocation deviation value of the layer is calculated. When the deviation value exceeds a predetermined threshold, the layer is determined to be a layer with unbalanced resource scheduling and is marked as a conflict analysis layer.

[0009] Preferably, the load conflict information of each conflict analysis layer is obtained, analyzed, and the conflict degree of each conflict analysis layer is evaluated when resource scheduling imbalance occurs, and the layers are classified. Specifically, this includes the following steps: Obtain load conflict information for each conflict analysis layer and perform preprocessing after acquisition; The dynamic state information of supply and demand and the power health assessment information are extracted from the load conflict information of each conflict analysis layer after preprocessing. After extraction, the information is analyzed to generate the supply and demand imbalance index and power quality index of each conflict analysis layer. A conflict assessment model is constructed for the supply-demand imbalance index and power quality index of each conflict analysis layer, and the conflict coefficient of each conflict analysis layer is generated by weighted summation. A pre-defined threshold range for the conflict coefficient is determined and compared with the conflict coefficients of each generated conflict analysis layer. Based on the comparison results, the degree of conflict of each conflict analysis layer when resource scheduling imbalance occurs is evaluated, and each conflict analysis layer is divided into a coordination balance layer, a scheduling tension layer, and a conflict imbalance layer based on the evaluation results.

[0010] Preferably, the logic for obtaining the supply-demand imbalance index of each conflict analysis layer is as follows: Dynamic supply and demand information is extracted from the preprocessed load conflict information of each conflict analysis layer. Specifically, this includes the actual total power supply, total power demand, and power fed back to the distribution network for each load unit in each conflict analysis layer, and these are categorized as follows: , and , Indicates the first In the first conflict analysis layer The actual total power supply of each load unit Indicates the first In the first conflict analysis layer The total electricity demand of each load unit. Indicates the first In the first conflict analysis layer The electrical power fed back to the distribution network by each load unit , , and All are positive integers; The supply-demand imbalance index for each conflict analysis layer is calculated using the following formula: ; In the formula, For the first The supply and demand imbalance index of the conflict analysis layer.

[0011] Preferably, the logic for obtaining the power quality index of each conflict analysis layer is as follows: Power health assessment information is extracted from the preprocessed load conflict information of each conflict analysis layer. Specifically, this includes the rated voltage value that each load unit in each conflict analysis layer should maintain under normal operating conditions, the actual operating voltage value, and the real-time measured total harmonic distortion rate (THD). These are then calibrated as follows: , and , Indicates the first In the first conflict analysis layer The rated voltage value that each load unit should maintain under normal operating conditions. Indicates the first In the first conflict analysis layer The actual operating voltage value of each load unit Indicates the first In the first conflict analysis layer The total harmonic distortion rate of the voltage measured in real time by each load unit , , and All are positive integers; The power quality index for each conflict analysis layer is calculated using the following formula: ; In the formula, For the first The power quality index of the conflict analysis layer.

[0012] Preferably, the supply and demand imbalance index of each generated conflict analysis layer is... and power quality index A conflict severity assessment model is constructed, and the conflict coefficients for each conflict analysis layer are generated through weighted summation. The specific calculation formula is as follows: ; In the formula, For the first The conflict coefficient of each conflict analysis layer and These are the supply and demand imbalance indices for each conflict analysis layer. and power quality index The non-zero weight coefficients, and .

[0013] Preferably, a pre-defined threshold range for the conflict coefficient is determined. And after being determined, the conflict coefficients of each conflict analysis layer are compared. A comparison was conducted, and the degree of conflict at each conflict analysis layer was assessed based on the comparison results when resource scheduling imbalances occurred. Based on the assessment results, each conflict analysis layer was divided into a coordination and balance layer, a scheduling tension layer, and a conflict imbalance layer. The specific comparison analysis and division are as follows: like When resource allocation imbalance occurs, the conflict level of this conflict analysis layer is low, and this conflict analysis layer is divided into a coordination and balance layer. like When resource scheduling imbalance occurs, the conflict level of this conflict analysis layer is medium, and this conflict analysis layer is classified as a scheduling tension layer. like When resource scheduling imbalance occurs, the conflict level of this conflict analysis layer is classified as a severe conflict level, and this conflict analysis layer is divided into a conflict imbalance layer.

[0014] Preferably, based on the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are implemented, specifically as follows: For the conflict analysis layer, which is divided into a coordination and balancing layer, the specific load scheduling collaborative optimization measures are as follows: continuously monitor the power demand data and power supply status data of each load unit in the conflict analysis layer in real time. When the power demand change exceeds the preset change threshold, adjust the resource allocation of the corresponding load unit to keep the load scheduling strategy consistent with the real-time operating status. For the conflict analysis layer, which is classified as the scheduling tension layer, the specific load scheduling collaborative optimization measures are as follows: based on the priority level of each load unit in the conflict analysis layer, reduce the power allocation of low-priority load units and increase the power allocation of high-priority load units, and reallocate resources according to the priority control strategy to reduce load competition and improve the supply and demand matching level. For the conflict analysis layer, which is classified as a conflict imbalance layer, the specific load scheduling collaborative optimization measures are as follows: start the backup power system associated with the conflict analysis layer, call the energy storage resources configured in the conflict analysis layer to output power, and at the same time perform load reduction operations on low-priority load units, give priority to ensuring the power supply needs of high-priority load units, and restore the supply and demand balance state in the conflict analysis layer.

[0015] Preferably, a hierarchical collaborative optimization system for a power distribution network includes a conflict sensing module, a hierarchical identification module, a conflict assessment module, an optimization execution module, and a dynamic adjustment module; The conflict detection module monitors the electricity demand data and corresponding priority information of each load unit in real time during the high load period of the distribution network to determine whether there is a load demand priority conflict in which load units with different priorities compete for the same power resources in the same time period. The hierarchical identification module constructs a hierarchical structure of the distribution network when it determines that there is a conflict in load demand priority. It monitors and analyzes the actual power supply information of each load unit in each layer in real time, filters out layers with unbalanced resource scheduling, and marks them as conflict analysis layers. The conflict assessment module acquires and analyzes the load conflict information of each conflict analysis layer, assesses the degree of conflict of each conflict analysis layer when resource scheduling imbalance occurs, and classifies them. The optimization execution module executes corresponding load scheduling collaborative optimization measures based on the classification results of each conflict analysis layer. The dynamic adjustment module monitors the power supply status and resource allocation changes of each load unit in each layer in real time after implementing load dispatching collaborative optimization measures, and dynamically adjusts the load dispatching strategy based on the real-time monitoring results.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention employs a hierarchical collaborative optimization method to accurately identify and address resource competition and priority conflicts among different load units during high-load periods in a distribution network. By monitoring the electricity demand data and priority information of load units in real time, the system can promptly detect conflicts where different priority load units compete for power resources simultaneously. Furthermore, through the construction of a hierarchical structure and the identification of resource scheduling imbalances, the resource allocation problem is localized and specified, enabling the distribution network to adopt more precise scheduling strategies. This process effectively improves the accuracy of load scheduling during high-load periods in the distribution network, avoiding the inefficiencies and imbalances caused by the global resource scheduling of traditional methods.

[0017] 2. During high-load periods, differences in the priorities of different load units often lead to conflicts in power resource allocation. This technical solution evaluates each conflict analysis layer using a supply-demand imbalance index and a power quality index, quantifying the degree of conflict at each layer and classifying the conflict levels. This allows the system to implement targeted optimization measures based on different conflict levels, such as adjusting the power allocation of low-priority load units and prioritizing the power supply needs of high-priority load units. Through this differentiated scheduling strategy, the problem of low-priority loads excessively consuming resources is avoided, while maximizing the power supply to high-priority loads, thereby improving the resource utilization efficiency of the distribution network, reducing resource waste, and optimizing the balance of load dispatching.

[0018] 3. This invention ensures the stable operation of the distribution network during peak load periods through dynamic monitoring and real-time adjustment. When resource imbalances occur, the system can respond quickly by activating backup power sources, utilizing energy storage resources, and reducing low-priority loads to rapidly restore the system's supply-demand balance, thus preventing power supply interruptions and outages caused by scheduling imbalances. In particular, through dynamic adjustments at each conflict analysis layer, the system can flexibly optimize load scheduling based on real-time feedback, further improving the grid's resilience and emergency response capabilities, thereby ensuring system stability and reliability even in extreme conditions. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a flowchart illustrating a hierarchical collaborative optimization method and system for power distribution networks according to the present invention.

[0021] Figure 2This is a schematic diagram of the modules of a hierarchical collaborative optimization method and system for power distribution networks according to the present invention. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] This invention provides, for example Figure 1 The hierarchical collaborative optimization method for distribution networks shown includes the following steps: During periods of high load in the distribution network, real-time monitoring of the electricity demand data and corresponding priority information of each load unit is conducted to determine whether there are load demand priority conflicts where load units with different priorities compete for the same power resources in the same time period. To monitor the electricity demand data and corresponding priority information of each load unit in real time during high-load periods in the distribution network, a load data acquisition and processing platform can be constructed. Specifically, firstly, existing monitoring equipment such as smart meters and remote terminal units (RTUs) installed in the load units periodically uploads real-time electricity consumption data to the distribution network data center. Then, at the software level, a data aggregation and preprocessing module is deployed to group the real-time collected data using time windows (e.g., 1-minute or 5-minute granularity). Simultaneously, a priority tag corresponding to each load unit is established in the system database (e.g., preset priorities based on user type, contractual agreements, and grid dispatching strategies). The data aggregation module correlates the electricity demand data and priority information in real time, forming a complete load unit monitoring dataset. This enables real-time, synchronous monitoring of all load units during high-load periods, providing fundamental data support for subsequent conflict identification.

[0024] After associating real-time data and priority information of load units, a conflict detection module can be constructed to perform parallel analysis of data from all load units within the same area on a time-slice basis (e.g., every minute). Specifically, the system first divides power resources into several regional resource pools, each with a certain capacity limit. Within each time slice, the conflict detection module compares the real-time power demand of each load unit with the remaining allocable capacity of its respective regional resource pool, and simultaneously sorts the load units according to their priority information. If, within the same resource pool, multiple load units with different priorities have a combined power demand exceeding the remaining capacity of the resource pool, and a high-priority load unit is unable to meet its power demand due to being occupied by a low-priority load unit, then a load demand priority conflict has occurred. The software system dynamically updates the conflict identification results within each time slice, providing a basis for subsequent hierarchical optimization.

[0025] The reason for needing to monitor the electricity demand and priority information of load units in real time and dynamically identify priority conflicts is that during high-load periods in the distribution network, electricity resources are inherently scarce and competitive, and the priority differences between load units directly determine the rationality of resource allocation. Without correlated monitoring of electricity demand and priority information, the power grid dispatch system will be unable to identify which load units are competing for resources and which should be prioritized. This can easily lead to low-priority loads preempting resources, while high-priority loads suffer from insufficient power supply, ultimately causing localized power outages, load imbalances, and even systemic power supply accidents. Through software-based real-time monitoring and conflict detection mechanisms, it is possible to proactively perceive and accurately define resource allocation conflicts during high-load periods. This provides accurate input conditions for subsequent hierarchical collaborative optimization based on conflict levels, thereby maximizing the safety, reliability, and fairness of power grid operation.

[0026] When it is determined that there is a conflict in load demand priority, a hierarchical structure of the distribution network is constructed, and the actual power supply information of each load unit in each layer is monitored and analyzed in real time. Layers with unbalanced resource scheduling are selected and marked as conflict analysis layers. In this embodiment, when it is determined that there is a conflict in load demand priority, a hierarchical structure of the distribution network is constructed. Specifically, based on the voltage level, geographical area, and physical connection relationship of the load units, all load units are divided into layers according to the preset hierarchical rules. Each layer corresponds to several load units, and the load unit identifier and real-time power demand data corresponding to each layer are recorded. Each layer is divided into load units according to the voltage level and geographical area. When load demand priority conflicts are identified, a hierarchical structure can be constructed at the software level by building a distribution network hierarchical management module. The specific implementation is as follows: First, basic attribute data of each load unit within the distribution network is collected through an interface, including the transformer number, voltage level, geographical location code, and physical connection information. Then, in the data processing module, data is logically divided according to preset hierarchical rules. These rules include: initial classification based on voltage level, for example, assigning load units with different voltage levels (110kV, 35kV, 10kV, 0.4kV, etc.) to different primary hierarchies; within load units of the same voltage level, further subdivision is made based on geographical area codes, such as the same power supply area or the area under the same substation, into secondary hierarchies. During the division process, the system establishes a load unit index table, mapping each load unit to its hierarchical identifier, forming a hierarchical mapping table. After hierarchical completion, the software system records the identifier information of all load units within each hierarchical layer and their corresponding real-time power demand data in real time, storing this data in a structured data format in the hierarchical database for subsequent monitoring and analysis of the load status of each layer. The entire hierarchical process is executed automatically, and the hierarchical structure can be updated in real time according to the dynamic changes of the load units, ensuring the consistency and real-time performance of the system structure.

[0027] The reason for reorganizing the load units of the distribution network through a hierarchical approach when load demand priority conflicts are identified is primarily because, under high-load and frequent conflict operating environments, the distribution network as a whole is a complex system. Directly optimizing the entire network in a uniform, flat manner would lead to problems such as massive data volume, slow response speed, and distorted decision-making. By dividing the network into hierarchical layers based on voltage levels, geographical regions, and physical connections, the optimization scope can be reasonably limited according to the physical attributes and geographical characteristics of each layer, reducing the scale and complexity of the optimization process. Furthermore, it allows for greater consistency and controllability in the operating characteristics of load units within each layer. This enables more precise and differentiated optimization strategies for different layers when performing load scheduling optimization and resource allocation adjustments, improving the system's response speed and handling effectiveness for local conflict issues. In addition, the hierarchical structure allows for parallel processing; conflict detection and scheduling optimization in each layer can run independently, significantly improving overall scheduling efficiency and system stability, effectively supporting the dynamic collaborative optimization requirements under complex load conflict scenarios.

[0028] The actual power supply information of each load unit in each layer is monitored and analyzed in real time. Layers with unbalanced resource scheduling are selected and marked as conflict analysis layers. Specifically, in each layer, the actual power supply information of the load unit is collected in real time and compared with the maximum load demand standard of the layer. The resource allocation deviation value of the layer is calculated. When the deviation value exceeds a predetermined threshold, the layer is determined to be a layer with unbalanced resource scheduling and is marked as a conflict analysis layer.

[0029] Real-time monitoring and analysis of the actual power supply information of each load unit in each tier can be achieved by deploying a tiered load monitoring and analysis module. Specifically, the system first uploads the actual power supply information of each load unit in real time through intelligent monitoring devices, including instantaneous power, active power, and reactive power, and then aggregates and manages this information in a database according to a tiered structure. For each tier, the data processing module automatically calculates the total actual power supply of all load units within that tier based on a preset time period (e.g., every 5 minutes), and simultaneously queries the maximum load demand standard corresponding to that tier. The maximum load demand standard can be obtained through historical data analysis, planning data modeling, or operational forecasting. Subsequently, the system uses a difference analysis module to calculate the resource allocation deviation value for each tier, using the formula: (Total Actual Power Supply - Maximum Load Demand Standard) / Maximum Load Demand Standard. The software system monitors the deviation value in real time each period and compares it with a preset resource scheduling balance threshold. If the deviation value exceeds the threshold, the system automatically marks the corresponding tier as having a resource scheduling imbalance and designates it as a conflict analysis tier, entering the subsequent conflict assessment and processing flow. The entire monitoring, calculation, and screening process is completed automatically in the data processing platform without human intervention, achieving efficient and real-time intelligent judgment.

[0030] The reason for collecting real-time power supply information of load units within each layer and performing deviation analysis to screen layers exhibiting resource scheduling imbalances is primarily because the load distribution in the distribution network is highly dynamic during high-load periods or under conflict conditions, and the real-time power supply of load units fluctuates continuously. Relying solely on static settings or preliminary priority judgments cannot accurately identify areas where load supply and demand imbalances occur due to unreasonable resource allocation during actual operation. This can easily lead to severe overload or insufficient power supply in some layers, resulting in wider-ranging power instability. Real-time data collection and deviation calculation not only dynamically capture the current load status of each layer but also quantitatively assess whether the resource scheduling of each layer is within a reasonable range. By comparing the deviation degree with the maximum load demand standard, layers with genuine problems can be quickly screened, ensuring that subsequent system optimization actions are targeted and accurate. This effectively prevents resource waste and power shortages, improving the stability and intelligent scheduling capabilities of the entire distribution network system. This approach not only enhances the system's real-time responsiveness but also achieves a unified judgment standard across layers and regions through standardized deviation indicators, providing a reliable foundation for subsequent collaborative optimization.

[0031] Obtain load conflict information for each conflict analysis layer, analyze it, assess the degree of conflict in each conflict analysis layer when resource scheduling imbalance occurs, and classify them. In this embodiment, load conflict information of each conflict analysis layer is obtained, analyzed, and the conflict degree of each conflict analysis layer when resource scheduling imbalance occurs is evaluated and classified. Specifically, the following steps are included: Obtain load conflict information for each conflict analysis layer and perform preprocessing after acquisition; In practical applications, load conflict information for each conflict analysis layer can be acquired through distribution network data acquisition and management systems (such as Distribution Automation System (DMS) and Energy Management System (EMS)). Specifically, firstly, the system's internal data interface module extracts power supply data, load demand data, real-time voltage monitoring data, and power quality index data for each load unit under the hierarchical structure from a real-time database. This data typically originates from smart meters, monitoring terminals, transformer terminals (TTUs), and distribution monitoring equipment installed on the load unit side. Then, on the data acquisition server, the acquired data is automatically categorized into the corresponding conflict analysis layer according to pre-defined hierarchical identifiers, and a timestamp is added simultaneously to ensure data real-time performance and time sequence consistency. During data acquisition, the system can set a sampling period (e.g., 1 minute, 5 minutes, etc.) and automatically batch-fetch the latest data snapshots within the specified period, forming a structured set of load conflict information for the conflict analysis layer, providing complete real-time data support for subsequent analysis and processing.

[0032] The main purpose of preprocessing is to improve the accuracy of subsequent data analysis, increase the efficiency of modeling and calculation, and prevent abnormal data from interfering with the conflict assessment results. Since load conflict information collected from field equipment contains noise, missing values, duplicates, and outdated timestamps, direct analysis and modeling without system preprocessing will lead to assessment bias or even incorrect decisions. At the software implementation level, preprocessing typically includes the following steps: First, data cleaning is performed to remove obvious outliers (such as negative power supply or voltage values ​​exceeding physical limits), and missing data is filled using nearest neighbor interpolation or moving average methods. Second, a unified data time base is established by using time alignment algorithms (such as resampling based on a sampling time window) to ensure that the data of all load units are at the same time. Then, data fields from different sources are standardized (e.g., power supply units are standardized to kW, and voltage standards are standardized to a percentage of the nominal value) to ensure consistent dimensions in subsequent formula calculations. Finally, through hierarchical filtering logic, only the data of the actually associated load units within each conflict analysis layer is retained, while invalid or externally interfering node data is removed. All preprocessing operations are performed automatically by the data processing module without human intervention, thus ensuring that the input data for the conflict analysis layer is both complete and accurate, and has a unified structural standard, meeting the stringent requirements for conflict degree assessment modeling.

[0033] The dynamic state information of supply and demand and the power health assessment information are extracted from the load conflict information of each conflict analysis layer after preprocessing. After extraction, the information is analyzed to generate the supply and demand imbalance index and power quality index of each conflict analysis layer. Extracting supply and demand dynamic status information and power health assessment information from the pre-processed load conflict information of each conflict analysis layer can be achieved by configuring a feature extraction module in the data processing platform. Specifically, after receiving the cleaned and standardized conflict analysis layer data, the system first filters out data fields related to the supply and demand dynamic status according to predefined field mapping rules, including the real-time power supply, current load demand, and load feedback of each load unit. Simultaneously, it filters out data fields related to power health assessment, including the real-time voltage value, standard voltage value, and real-time harmonic content of each load unit. During the extraction process, the system performs batch traversal based on the conflict analysis layer identifier, classifies and aggregates the data of each layer node, and structures data of the same type into a unified format of supply and demand dynamic status information set and power health assessment information set, automatically adding corresponding timestamps and hierarchical labels to ensure information consistency and traceability. To improve processing efficiency, the system adopts an asynchronous parallel processing strategy, which enables data extraction and classification to be completed simultaneously in multiple conflict analysis layers. This ensures the independence and integrity of supply and demand dynamic status information and power health assessment information across different conflict analysis layers, thus laying a solid data foundation for the subsequent generation of supply and demand imbalance indices and power quality indices for each conflict analysis layer.

[0034] A conflict assessment model is constructed for the supply-demand imbalance index and power quality index of each conflict analysis layer, and the conflict coefficient of each conflict analysis layer is generated by weighted summation. A pre-defined threshold range for the conflict coefficient is determined and compared with the conflict coefficients of each generated conflict analysis layer. Based on the comparison results, the degree of conflict of each conflict analysis layer when resource scheduling imbalance occurs is evaluated, and each conflict analysis layer is divided into a coordination balance layer, a scheduling tension layer, and a conflict imbalance layer based on the evaluation results.

[0035] To determine the pre-defined threshold range for conflict coefficients, this can be achieved within the distribution network data management platform through a historical operational data analysis module. Specifically, firstly, the system backtracks and extracts the generated conflict coefficient dataset based on load conflict information from each conflict analysis layer within a historical period (e.g., the past 6 or 12 months). Subsequently, statistical analysis algorithms are used to model and analyze the distribution characteristics of the conflict coefficients, including calculating statistical indicators such as mean, standard deviation, median, and quantiles, and generating frequency distribution curves or probability density distribution curves for the conflict coefficients. Based on this, the software system sets the threshold range for the coordination and balance layer according to predefined hierarchical standards. For example, the lower interval of the conflict coefficient distribution (e.g., 0-30% quantile) is set as the threshold range for the scheduling tension layer, the middle interval (e.g., 30%-70% quantile) as the threshold range for the conflict imbalance layer, and the higher interval (e.g., 70%-100% quantile) as the threshold range for the conflict imbalance layer. The system can flexibly adjust the boundary points of each interval according to actual needs and supports dynamic retraining to adapt to changes in distribution network operating characteristics. The entire determination process is based on data statistical analysis and quantile standard segmentation strategy, and is completed automatically by the software platform without human intervention. This ensures that the conflict coefficient threshold range setting is objective, adaptive, and highly adaptable, providing a scientific basis for subsequent classification and evaluation.

[0036] In this embodiment, the logic for obtaining the supply and demand imbalance index of each conflict analysis layer is as follows: Dynamic supply and demand information is extracted from the preprocessed load conflict information of each conflict analysis layer. Specifically, this includes the actual total power supply, total power demand, and power fed back to the distribution network for each load unit in each conflict analysis layer, and these are categorized as follows: , and , Indicates the first In the first conflict analysis layer The actual total power supply of each load unit Indicates the first In the first conflict analysis layer The total electricity demand of each load unit. Indicates the first In the first conflict analysis layer The electrical power fed back to the distribution network by each load unit , , and All are positive integers; The actual total power supply, total power demand, and power fed back to the distribution network for each load unit in each conflict analysis layer can be collected and centrally processed in real time through the collaboration of the distribution network energy management platform (EMS) and intelligent monitoring systems (such as smart meters, distributed energy monitoring terminals, and energy storage management systems). Specifically, the actual total power supply ( This refers to the actual amount of electrical energy supplied by the power grid to each load unit. It can be obtained by measuring the cumulative active energy at the load unit's port using a smart meter. The system periodically reads and synchronizes this data into the database according to a set time period (e.g., every 5 minutes). Total electricity demand ( This refers to the projected electricity demand calculated by load units based on historical electricity consumption behavior, current operating status, and prediction algorithms. The system dynamically generates this data through the intelligent load management module, combining real-time monitoring data and prediction models, and updates it to the load unit database in real time. The electricity fed back to the distribution network (…) This refers to the power generated by load units equipped with distributed generation facilities (such as photovoltaic inverters and wind turbines) or energy storage devices (such as battery systems), which feed surplus electricity back to the distribution network after meeting their own electricity needs. The system directly reads the data streams from the inverters, electricity meter feedback terminals, or energy storage output recording terminals. After collection, all data is automatically aggregated to the corresponding conflict analysis layer through hierarchical identification and uniformly timestamped to ensure data synchronization and consistency. At the software processing level, the data management module achieves seamless data flow and processing through interface calls, periodic scheduling, and automatic database entry mechanisms, ensuring that the actual total power supply, total electricity demand, and fed-back power are obtained completely, in real time, and traceably, providing a solid foundation for the accurate calculation of the subsequent supply-demand imbalance index.

[0037] The supply-demand imbalance index for each conflict analysis layer is calculated using the following formula: ; In the formula, For the first The supply and demand imbalance index of the conflict analysis layer.

[0038] Supply and demand imbalance index The calculation formula aims to quantify the degree of supply and demand imbalance at each conflict analysis layer. The logarithmic function and absolute value operation in the formula work together to ensure accurate capture of the deviation between power supply and demand. First, the absolute value in the formula ( The actual power supply was processed. ) and feedback power ( ) and electricity demand ( The difference between () and (). The purpose of this operation is to accurately reflect the absolute deviation of the supply-demand imbalance regardless of whether the power supply is excessive or insufficient, ensuring that positive and negative deviations do not cancel each other out, thus providing a more accurate measure of imbalance. Next, the introduction of the logarithmic function helps to compress excessively large or small deviation values, avoiding the excessive influence of extreme cases on the results, especially when the supply-demand imbalance is minor, it can sensitively capture subtle changes. In actual calculations, the logarithmic operation in the formula makes It is more sensitive to small supply-demand imbalances, while appropriately mitigating the computational impact of large supply-demand differences, resulting in a smoother and more stable overall system evaluation. Furthermore, weighted summation averages the supply-demand imbalance for each load unit, ensuring a balanced contribution of load units within each conflict analysis layer to the overall evaluation, avoiding excessive influence from anomalous data in some load units. Finally, by calculating the average value, the imbalance within each conflict analysis layer can be effectively and comprehensively evaluated, ensuring... It is highly representative and practical, and applicable to subsequent resource scheduling optimization decisions.

[0039] According to the supply and demand imbalance index The calculation formula, The magnitude of this value directly reflects the degree of deviation in the overall supply-demand matching of load units in each conflict analysis layer. When the actual total power supply of each load unit ( ) and total electricity demand ( The differences between them are small, and the power fed back to the distribution network is also small. When it can effectively offset local supply and demand fluctuations, The calculation results tend to be smaller, indicating that the resource scheduling of this conflict analysis layer is relatively balanced and the degree of conflict is minor; conversely, when there is a large deviation between the total power supply and the load demand, and even after considering the feedback power, there is still a significant supply-demand mismatch, the imbalance deviation is amplified after absolute value processing, and compressed by the logarithmic function. The still high values ​​indicate a severe imbalance in resource allocation at this conflict analysis layer, suggesting a high level of conflict. Therefore, The smaller the value, the better the supply and demand coordination of the conflict analysis layer, the higher the resource allocation balance, and the milder the conflict. The larger the value, the more severe the supply-demand imbalance, the more prominent the contradictions in resource allocation, and the more serious the conflict, requiring priority intervention in load scheduling optimization and resource reallocation. Through this quantitative relationship, the system can... Numerical values ​​are used to accurately assess the severity of conflicts in resource scheduling at each conflict analysis layer, and to classify different optimization priorities accordingly.

[0040] In this embodiment, the logic for obtaining the power quality index of each conflict analysis layer is as follows: Power health assessment information is extracted from the preprocessed load conflict information of each conflict analysis layer. Specifically, this includes the rated voltage value that each load unit in each conflict analysis layer should maintain under normal operating conditions, the actual operating voltage value, and the real-time measured total harmonic distortion rate (THD). These are then calibrated as follows: , and , Indicates the first In the first conflict analysis layer The rated voltage value that each load unit should maintain under normal operating conditions. Indicates the first In the first conflict analysis layer The actual operating voltage value of each load unit Indicates the first In the first conflict analysis layer The total harmonic distortion rate of the voltage measured in real time by each load unit , , and All are positive integers; The rated voltage, actual operating voltage, and real-time measured total harmonic distortion (THD) of each load unit in each conflict analysis layer under normal operating conditions can be collected and centrally processed through the collaborative efforts of distribution network intelligent monitoring systems (such as Advanced Measurement Infrastructure (AMI), Distribution Automation System (DAS), and Power Quality Monitoring Terminal (PQM)). Specifically, the rated voltage ( This refers to the target voltage value that a load unit should maintain under normal operating conditions according to the voltage levels (such as 10kV, 380V, etc.) specified in the power distribution network design specifications. The system is configured during the design phase using a network model database (GIS, SCADA system), with each load unit corresponding to a fixed rated voltage standard; the actual operating voltage value ( ) refers to the voltage data of the power supply port measured by the load unit during real-time operation. This data is typically collected in real-time at second or minute intervals by voltage monitoring modules installed on the low-voltage side of the distribution transformer, at the grid connection point of the distributed power source, or within the terminal smart meter. The data is then transmitted to a central server for archiving and management via a data communication system. The total harmonic distortion (THD) is... The power quality index (HQI) represents the degree of distortion of all non-fundamental components in the actual voltage waveform. This is typically detected periodically by power quality monitoring devices equipped with harmonic analysis capabilities or by the power quality module in smart meters. The sampling frequency is usually 1 minute or 5 minutes, and the data is reported to the distribution management system in real time. After all the above data is collected, the software platform receives it uniformly through the data access module and automatically marks it to the corresponding level according to the conflict analysis layer grouping strategy. This completes structured storage and timestamp synchronization, providing complete, standardized, and real-time reliable data support for the accurate calculation of the power quality index.

[0041] The power quality index for each conflict analysis layer is calculated using the following formula: ; In the formula, For the first The power quality index of the conflict analysis layer.

[0042] Power quality index of each conflict analysis layer Calculated using a specific formula, this aims to comprehensively quantify the voltage stability and power quality health of each load unit. Firstly, the formula... In this section, the actual operating voltage value of each load unit was calculated. ) and rated voltage value ( The relative deviation between the two is squared to amplify its effect, ensuring that even small voltage deviations are sensitively detected and eliminating the problem of positive and negative deviations canceling each other out; secondly, Partially, the total harmonic distortion of voltage (THD) After standardization and squaring, harmonic distortion factors in power quality are introduced to ensure a comprehensive consideration of power distortion. Furthermore, the square amplifies the impact of high-harmonic load units, improving the system's ability to identify power degradation. Then... The steps involve summing the averages of the two types of deviations from all load units within each conflict analysis layer to obtain the overall power health level of that layer, preventing individual node anomalies from affecting the global assessment. Finally, the overall average is processed through exponential operations. The exponential function amplifies the scores of layers with more severe power quality problems, ensuring that conflict analysis layers with significant power degradation are highly sensitively distinguished in the exponential results, enhancing the clarity of classification and intervention priority judgment. Through this computational design, It can simultaneously reflect two core power quality indicators: voltage amplitude stability and harmonic distortion. Furthermore, in terms of mathematical characteristics, it ensures sensitivity to small deviations and significant impact on large deviations, thereby ensuring that the system can efficiently and accurately identify and quantify the power health status of each conflict analysis layer.

[0043] No. Power quality index of each conflict analysis layer The numerical value directly reflects the overall stability of power supply and the health level of power quality in this hierarchical node. When the actual operating voltage value of each load unit within the conflict analysis layer ( ) and rated voltage value ( The deviation between them is small, and the total harmonic distortion of the voltage measured in real time (THD) is also small. When the exponential function's output is low, after normalization, squaring, and mean calculation, the output will tend to be smaller, indicating that the power supply at this conflict analysis layer is stable, the power quality is good, the system operates smoothly, resource scheduling is balanced, and the conflict level is mild. Conversely, when load units generally have large voltage deviations or high harmonic distortion rates, even after normalization, these deviations and distortions are amplified in the squaring operation. Combined with the exponential function's further amplification of mean anomalies, this ultimately leads to... The rapid increase in the value reflects a severe deterioration in power quality at this conflict analysis layer, a high degree of internal supply-demand imbalance, difficulties in dispatching and coordination, and a significant escalation of conflict. Therefore, The higher the value, the more unhealthy the power supply status of the conflict analysis layer, and the more severe the conflict. The smaller the value, the more stable the power state of the conflict analysis layer, the more balanced the resource scheduling, and the less severe the conflict. This is achieved through a method based on... The quantitative relationship of the numerical values ​​can accurately assess the health of resource scheduling at each conflict analysis layer, thus providing a strong basis for subsequent optimization interventions.

[0044] In this embodiment, the supply and demand imbalance index of each conflict analysis layer is generated. and power quality index A conflict severity assessment model is constructed, and the conflict coefficients for each conflict analysis layer are generated through weighted summation. The specific calculation formula is as follows:

[0045] In the formula, For the first The conflict coefficient of each conflict analysis layer and These are the supply and demand imbalance indices for each conflict analysis layer. and power quality index The non-zero weight coefficients, and .

[0046] In actual implementation, the system first uses the supply and demand imbalance index generated by each conflict analysis layer as a basis. and power quality index The conflict assessment module is invoked to calculate the conflict coefficient according to the set weighted summation model. The calculation involves multiplying the supply-demand imbalance index of each conflict analysis layer by a weighting coefficient based on preset weight parameters. Power quality index multiplied by weighting factor The two factors are then summed to form the final conflict coefficient. Both weighting coefficients are non-zero real numbers, and their sum equals 1. Used to reflect the relative importance of supply and demand imbalance factors in the assessment of the degree of conflict. This is used to reflect the intensity of the impact of power quality factors on the degree of conflict. Specific weight values ​​can be configured based on historical operating experience, system optimization goals, or expert experience. For example, in scenarios with drastic load dynamics but relatively stable voltage quality, the weight can be appropriately increased. The weighting of power quality can be appropriately increased in areas with significant power quality fluctuations and severe harmonic pollution. The weighting of the factors is determined by the appropriate setting of the weighting coefficients. This allows the conflict coefficients to dynamically adapt to the system characteristics under different operating environments, accurately reflecting the severity of supply and demand imbalances while also taking into account the stability of power quality. This provides a more scientific and accurate quantitative basis for subsequent conflict classification and load scheduling optimization.

[0047] In this embodiment, a pre-set conflict coefficient threshold range is determined. And after being determined, the conflict coefficients of each conflict analysis layer are compared. A comparison was conducted, and the degree of conflict at each conflict analysis layer was assessed based on the comparison results when resource scheduling imbalances occurred. Based on the assessment results, each conflict analysis layer was divided into a coordination and balance layer, a scheduling tension layer, and a conflict imbalance layer. The specific comparison analysis and division are as follows: like When resource allocation imbalance occurs, the conflict level of this conflict analysis layer is low, and this conflict analysis layer is divided into a coordination and balance layer. This situation indicates that when resource scheduling imbalances occur, the supply and demand matching of each load unit within this conflict analysis layer is good, power quality is stable, and the overall operation is coordinated and controlled. The system's scheduling resources can effectively meet the power demands of each load unit while maintaining a high level of power health. In this case, this conflict analysis layer is classified as a coordination and balancing layer, meaning that no additional intervention measures are required; only routine monitoring and dynamic adjustments are needed. Its impact is that this layer contributes positively to the stability of the entire distribution network system, does not trigger cascading load imbalances or power quality deterioration, and effectively supports the main grid's scheduling flexibility and overall optimization goals.

[0048] like When resource scheduling imbalance occurs, the conflict level of this conflict analysis layer is medium, and this conflict analysis layer is classified as a scheduling tension layer. This situation indicates that when resource scheduling imbalances occur, the supply-demand matching within this conflict analysis layer has deviated to some extent, resulting in a slight decline in power quality, intensified resource competition among load units, and increased scheduling pressure. At this point, this conflict analysis layer is classified as a scheduling-stressed layer, meaning that targeted scheduling optimization interventions are needed, such as appropriately adjusting load allocation ratios and prioritizing power supply to high-priority load units. The impact is that if optimization is not implemented in a timely manner, it may gradually evolve into a more severe supply-demand imbalance, increasing system scheduling complexity, reducing the flexibility and controllability of the overall distribution network operation, and consequently adversely affecting local power stability.

[0049] like When resource scheduling imbalance occurs, the conflict level of this conflict analysis layer is classified as a severe conflict level, and this conflict analysis layer is divided into a conflict imbalance layer.

[0050] This situation indicates that when resource scheduling imbalances occur, there is a severe supply-demand imbalance within this conflict analysis layer, with significant power quality degradation and sharp conflicts in resource allocation among load units, making it difficult for the system to maintain operational balance through conventional scheduling methods. At this point, this conflict analysis layer is classified as a conflict imbalance layer, meaning that mandatory optimization measures must be taken immediately, such as activating backup power sources, mobilizing energy storage resources, and reducing power supply to low-priority loads. The impact is that without rapid intervention, it can easily lead to localized load collapse, power outages, and even triggering distribution network protection actions, thereby posing a serious threat to the safe and stable operation of the entire power grid, causing widespread degradation of power supply reliability and economic losses.

[0051] Based on the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are implemented respectively; In this embodiment, based on the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are executed respectively, specifically as follows: For the conflict analysis layer, which is divided into a coordination and balancing layer, the specific load scheduling collaborative optimization measures are as follows: continuously monitor the power demand data and power supply status data of each load unit in the conflict analysis layer in real time. When the power demand change exceeds the preset change threshold, adjust the resource allocation of the corresponding load unit to keep the load scheduling strategy consistent with the real-time operating status. For the conflict analysis layer, which is divided into a coordination and balancing layer, continuous real-time monitoring of the electricity demand and power supply status data of each load unit is implemented. When the change in electricity demand exceeds a preset threshold, resource allocation is adjusted. This can be achieved through a software monitoring module integrated into the load dispatching management platform. Specifically, the system first configures a real-time data access interface for each load unit. The acquisition module periodically collects electricity demand data (such as instantaneous load and average load) and power supply status data (such as supply voltage and active power supply), and archives this data to the hierarchical monitoring database in real time. Subsequently, the data analysis module compares the latest collected data of each period with the data of the previous reference period to calculate the change magnitude, and performs dynamic comparison based on the set change threshold standard. When the change in electricity demand of a load unit exceeds the preset threshold, the system automatically triggers the resource adjustment logic, calls the dispatching control module to increase or decrease the amount of power resources allocated to that load unit, and updates the dispatching instructions to ensure that the overall load dispatching strategy reflects changes in operating status in real time. By continuously monitoring and dynamically fine-tuning in this way, we can avoid the problem of over- or under-allocation of resources caused by small fluctuations in load units, ensure that the system can respond quickly in the early stages of local load changes, maintain a dynamic balance between resource supply and demand, thereby further stabilizing and coordinating the overall operating status of the balance layer, reducing the fluctuation of dispatch pressure caused by the accumulation of load changes, and improving the dispatch stability and flexibility of the distribution network system.

[0052] For the conflict analysis layer, which is classified as the scheduling tension layer, the specific load scheduling collaborative optimization measures are as follows: based on the priority level of each load unit in the conflict analysis layer, reduce the power allocation of low-priority load units and increase the power allocation of high-priority load units, and reallocate resources according to the priority control strategy to reduce load competition and improve the supply and demand matching level. For the conflict analysis layer, which is classified as a scheduling tension layer, adjusting the power allocation and reallocating resources based on the priority level of each load unit can be achieved through the priority scheduling optimization module in the distribution network intelligent dispatch control platform. Specifically, the system first calls the priority level information of each load unit within the conflict analysis layer and sorts all load units according to a preset priority ranking table. Then, it reads the current power allocation of each load unit in real time and matches it with its priority level, using difference analysis logic to determine whether the resource allocation conforms to the priority control strategy. When the system detects that the power allocation of a low-priority load unit exceeds its deserved level, or that the power supply to a high-priority load unit is insufficient, the scheduling module performs an adjustment operation according to the priority weight model, reducing the power allocation of low-priority load units and releasing resources to supply high-priority load units. Finally, it reallocates the overall load resources within the conflict analysis layer according to the optimization objective function. This dynamic resource reallocation based on priority levels not only ensures the power supply needs of critical load units under tight scheduling conditions, but also effectively reduces scheduling pressure caused by resource competition, improves the coordination of supply and demand matching among load units, thereby preventing further deterioration of supply and demand imbalance and maintaining the overall operational stability and resource utilization efficiency of the distribution network.

[0053] For the conflict analysis layer, which is classified as a conflict imbalance layer, the specific load scheduling collaborative optimization measures are as follows: start the backup power system associated with the conflict analysis layer, call the energy storage resources configured in the conflict analysis layer to output power, and at the same time perform load reduction operations on low-priority load units, give priority to ensuring the power supply needs of high-priority load units, and restore the supply and demand balance state in the conflict analysis layer.

[0054] For the conflict analysis layer, which is classified as a conflict imbalance layer, the activation of backup power systems, the call for energy storage resources to output power, and the execution of load reduction operations on low-priority load units can be achieved through the software control module of the distribution network emergency response management platform. Specifically, the system first identifies the backup power systems (such as backup diesel generators, microgrid systems, etc.) and energy storage units (such as battery energy storage systems, BESS) associated with each layer node based on the node configuration files of the conflict analysis layer, and monitors their availability status in real time through the communication interface. When the backup power or energy storage system is detected to be available and the conflict level within the conflict analysis layer exceeds the severe threshold, the scheduling module automatically issues an activation command to activate the backup power and release the output of the energy storage unit to increase the power supply capacity within the conflict analysis layer. At the same time, according to the preset load priority list, the control module performs load reduction operations on low-priority load units, including power curtailment, capacity reduction, or phased power outages, to ensure that power resources are prioritized for high-priority load units and quickly restore the supply and demand balance of the conflict analysis layer. This multi-strategy parallel emergency optimization approach can maximize the supply of critical loads even in the event of severe resource shortages, prevent a wider range of grid chain reactions caused by the continued deterioration of supply and demand imbalances, significantly improve the emergency resilience and stability of the distribution network under extreme conflict conditions, and ensure the overall safe and reliable operation of the system.

[0055] After implementing load dispatching coordination optimization measures, the power supply status and resource allocation changes of each load unit in each layer are monitored in real time. The load dispatching strategy is dynamically adjusted based on the real-time monitoring results to optimize the load dispatching balance and system operation stability of the distribution network.

[0056] After implementing load dispatching collaborative optimization measures, real-time monitoring of the power supply status and resource allocation changes of each load unit in each layer can be achieved through the load dispatching real-time monitoring module and intelligent analysis engine in the distribution network integrated monitoring platform. Specifically, the load dispatching real-time monitoring module continuously acquires power supply status information (including actual power supply and power supply stability indicators) and resource allocation change information (including the latest resource allocation ratio and change magnitude) of each load unit in each conflict analysis layer through a preset data acquisition interface. After data preprocessing, the intelligent analysis engine performs dynamic trend analysis to identify in real time whether the power supply status and resource allocation status of each load unit are consistent with the established dispatching strategy. When a decrease in power supply stability, abnormal allocation ratio, or load supply deviating from the dispatching target is detected, the system triggers dynamic dispatching adjustment logic based on the analysis results, regenerates load allocation instructions, and sends them to the execution control layer to achieve real-time correction of the resource allocation of each load unit, so as to keep the overall dispatching scheme synchronized with the actual operating status.

[0057] The reason for continuous real-time monitoring and dynamic adjustment after the implementation of load dispatching coordination optimization measures is that the operating environment of the distribution network is highly dynamic and uncertain. Load demand and supply status may change rapidly due to external factors (such as temperature changes, fluctuations in user behavior, and fluctuations in distributed energy resources). Without continuous monitoring and timely dynamic adjustment, the original load dispatching strategy can easily become ineffective due to the accumulation of actual operational deviations, leading to a further deterioration of local supply-demand imbalances and even triggering new resource competition and power quality degradation problems. Through real-time monitoring and dynamic adjustment, a rapid response can be initiated when the load status initially deviates, resource allocation can be corrected in a timely manner, small problems can be prevented from spreading into systemic risks, the balance of load dispatching can be continuously optimized, and the stability, flexibility, and anti-disturbance capability of the overall distribution network operation can be maximized, thereby achieving the core technical objective of this invention: ensuring the healthy and efficient operation of the power grid in a variable environment.

[0058] like Figure 2 The distribution network hierarchical collaborative optimization system shown includes a conflict sensing module, a hierarchical identification module, a conflict assessment module, an optimization execution module, and a dynamic adjustment module; The conflict detection module monitors the electricity demand data and corresponding priority information of each load unit in real time during the high load period of the distribution network to determine whether there is a load demand priority conflict in which load units with different priorities compete for the same power resources in the same time period. The hierarchical identification module constructs a hierarchical structure of the distribution network when it determines that there is a conflict in load demand priority. It monitors and analyzes the actual power supply information of each load unit in each layer in real time, filters out layers with unbalanced resource scheduling, and marks them as conflict analysis layers. The conflict assessment module acquires and analyzes the load conflict information of each conflict analysis layer, assesses the degree of conflict of each conflict analysis layer when resource scheduling imbalance occurs, and classifies them. The optimization execution module executes corresponding load scheduling collaborative optimization measures based on the classification results of each conflict analysis layer. The dynamic adjustment module monitors the power supply status and resource allocation changes of each load unit in each layer in real time after implementing load dispatching collaborative optimization measures, and dynamically adjusts the load dispatching strategy based on the real-time monitoring results.

[0059] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0060] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0061] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0062] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0064] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0066] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A hierarchical collaborative optimization method for power distribution networks, characterized in that, The specific steps include: During periods of high load in the distribution network, real-time monitoring of the electricity demand data and corresponding priority information of each load unit is conducted to determine whether there are load demand priority conflicts where load units with different priorities compete for the same power resources in the same time period. When it is determined that there is a conflict in load demand priority, a hierarchical structure of the distribution network is constructed, and the actual power supply information of each load unit in each layer is monitored and analyzed in real time. Layers with unbalanced resource scheduling are selected and marked as conflict analysis layers. Obtain load conflict information for each conflict analysis layer, analyze it, assess the degree of conflict in each conflict analysis layer when resource scheduling imbalance occurs, and classify them. Based on the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are implemented respectively; After implementing load dispatching coordination optimization measures, the power supply status and resource allocation changes of each load unit in each layer are monitored in real time, and the load dispatching strategy is dynamically adjusted based on the real-time monitoring results.

2. The hierarchical collaborative optimization method for distribution networks according to claim 1, characterized in that, When a conflict in load demand priority is identified, a hierarchical structure for the distribution network is constructed. Specifically, based on the voltage level, geographical area, and physical connection relationship of the load units, all load units are divided into layers according to a preset hierarchical rule. Each layer corresponds to several load units, and the load unit identifier and real-time power demand data corresponding to each layer are recorded. Each layer is divided into load units according to voltage level and geographical area. The actual power supply information of each load unit in each layer is monitored and analyzed in real time. Layers with unbalanced resource scheduling are selected and marked as conflict analysis layers. Specifically, in each layer, the actual power supply information of the load unit is collected in real time and compared with the maximum load demand standard of the layer. The resource allocation deviation value of the layer is calculated. When the deviation value exceeds a predetermined threshold, the layer is determined to be a layer with unbalanced resource scheduling and is marked as a conflict analysis layer.

3. The hierarchical collaborative optimization method for distribution networks according to claim 2, characterized in that, Obtain load conflict information for each conflict analysis layer, analyze it, assess the conflict severity of each conflict analysis layer when resource scheduling imbalance occurs, and classify them. This process includes the following steps: Obtain load conflict information for each conflict analysis layer and perform preprocessing after acquisition; The dynamic state information of supply and demand and the power health assessment information are extracted from the load conflict information of each conflict analysis layer after preprocessing. After extraction, the information is analyzed to generate the supply and demand imbalance index and power quality index of each conflict analysis layer. A conflict assessment model is constructed for the supply-demand imbalance index and power quality index of each conflict analysis layer, and the conflict coefficient of each conflict analysis layer is generated by weighted summation. A pre-defined threshold range for the conflict coefficient is determined and compared with the conflict coefficients of each generated conflict analysis layer. Based on the comparison results, the degree of conflict of each conflict analysis layer when resource scheduling imbalance occurs is evaluated, and each conflict analysis layer is divided into a coordination balance layer, a scheduling tension layer, and a conflict imbalance layer based on the evaluation results.

4. The hierarchical collaborative optimization method for a distribution network according to claim 3, characterized in that, The logic for obtaining the supply and demand imbalance index of each conflict analysis layer is as follows: Dynamic supply and demand information is extracted from the preprocessed load conflict information of each conflict analysis layer. Specifically, this includes the actual total power supply, total power demand, and power fed back to the distribution network for each load unit in each conflict analysis layer, and these are categorized as follows: , and , Indicates the first In the first conflict analysis layer The actual total power supply of each load unit Indicates the first In the first conflict analysis layer The total electricity demand of each load unit. Indicates the first In the first conflict analysis layer The electrical power fed back to the distribution network by each load unit , , and All are positive integers; The supply-demand imbalance index for each conflict analysis layer is calculated using the following formula: ； In the formula, For the first The supply and demand imbalance index of the conflict analysis layer.

5. The hierarchical collaborative optimization method for distribution networks according to claim 4, characterized in that, The logic for obtaining the power quality index of each conflict analysis layer is as follows: Power health assessment information is extracted from the preprocessed load conflict information of each conflict analysis layer. Specifically, this includes the rated voltage value that each load unit in each conflict analysis layer should maintain under normal operating conditions, the actual operating voltage value, and the real-time measured total harmonic distortion rate (THD). These are then calibrated as follows: , and , Indicates the first In the first conflict analysis layer The rated voltage value that each load unit should maintain under normal operating conditions. Indicates the first In the first conflict analysis layer The actual operating voltage value of each load unit Indicates the first In the first conflict analysis layer The total harmonic distortion rate of the voltage measured in real time by each load unit , , and All are positive integers; The power quality index for each conflict analysis layer is calculated using the following formula: ； In the formula, For the first The power quality index of the conflict analysis layer.

6. The hierarchical collaborative optimization method for a distribution network according to claim 5, characterized in that, Supply and demand imbalance index for each generated conflict analysis layer and power quality index A conflict severity assessment model is constructed, and the conflict coefficients for each conflict analysis layer are generated through weighted summation. The specific calculation formula is as follows: ； In the formula, For the first The conflict coefficient of each conflict analysis layer and These are the supply and demand imbalance indices for each conflict analysis layer. and power quality index The non-zero weight coefficients, and .

7. The hierarchical collaborative optimization method for a distribution network according to claim 6, characterized in that, Determine the pre-set threshold range for the conflict coefficient. And after being determined, the conflict coefficients of each conflict analysis layer are compared. A comparison was conducted, and the degree of conflict at each conflict analysis layer was assessed based on the comparison results when resource scheduling imbalances occurred. Based on the assessment results, each conflict analysis layer was divided into a coordination and balance layer, a scheduling tension layer, and a conflict imbalance layer. The specific comparison analysis and division are as follows: like When resource allocation imbalance occurs, the conflict level of this conflict analysis layer is low, and this conflict analysis layer is divided into a coordination and balance layer. like When resource scheduling imbalance occurs, the conflict level of this conflict analysis layer is medium, and this conflict analysis layer is classified as a scheduling tension layer. like When resource scheduling imbalance occurs, the conflict level of this conflict analysis layer is classified as a severe conflict level, and this conflict analysis layer is divided into a conflict imbalance layer.

8. The hierarchical collaborative optimization method for a distribution network according to claim 7, characterized in that, Based on the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are implemented, specifically as follows: For the conflict analysis layer, which is divided into a coordination and balancing layer, the specific load scheduling collaborative optimization measures are as follows: continuously monitor the power demand data and power supply status data of each load unit in the conflict analysis layer in real time. When the power demand change exceeds the preset change threshold, adjust the resource allocation of the corresponding load unit to keep the load scheduling strategy consistent with the real-time operating status. For the conflict analysis layer, which is classified as the scheduling tension layer, the specific load scheduling collaborative optimization measures are as follows: based on the priority level of each load unit in the conflict analysis layer, reduce the power allocation of low-priority load units and increase the power allocation of high-priority load units, and reallocate resources according to the priority control strategy to reduce load competition and improve the supply and demand matching level. For the conflict analysis layer, which is classified as a conflict imbalance layer, the specific load scheduling collaborative optimization measures are as follows: start the backup power system associated with the conflict analysis layer, call the energy storage resources configured in the conflict analysis layer to output power, and at the same time perform load reduction operations on low-priority load units, give priority to ensuring the power supply needs of high-priority load units, and restore the supply and demand balance state in the conflict analysis layer.

9. A hierarchical collaborative optimization system for a distribution network, used to implement the hierarchical collaborative optimization method for a distribution network as described in any one of claims 1-8, characterized in that, It includes a conflict perception module, a hierarchical identification module, a conflict assessment module, an optimization execution module, and a dynamic adjustment module; The conflict detection module monitors the electricity demand data and corresponding priority information of each load unit in real time during the high load period of the distribution network to determine whether there is a load demand priority conflict in which load units with different priorities compete for the same power resources in the same time period. The hierarchical identification module constructs a hierarchical structure of the distribution network when it determines that there is a conflict in load demand priority. It monitors and analyzes the actual power supply information of each load unit in each layer in real time, filters out layers with unbalanced resource scheduling, and marks them as conflict analysis layers. The conflict assessment module acquires and analyzes the load conflict information of each conflict analysis layer, assesses the degree of conflict of each conflict analysis layer when resource scheduling imbalance occurs, and classifies them. The optimization execution module executes corresponding load scheduling collaborative optimization measures based on the classification results of each conflict analysis layer. The dynamic adjustment module monitors the power supply status and resource allocation changes of each load unit in each layer in real time after implementing load dispatching collaborative optimization measures, and dynamically adjusts the load dispatching strategy based on the real-time monitoring results.

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