A power distribution network layered collaborative optimization method and system

Abstract

The application discloses a power distribution network layered cooperative optimization method and system, relates to the technical field of power distribution network layered cooperation, and specifically comprises the following steps: when it is determined that there is a load demand priority conflict, a layered structure of the power distribution network is constructed, actual power supply information of each load unit in each layer is monitored and analyzed in real time, a layer in which a resource scheduling imbalance phenomenon occurs is screened, and the layer is marked as a conflict analysis layer; load conflict information of each conflict analysis layer is acquired, and the load conflict information is analyzed, the conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs is evaluated, and the conflict analysis layers are classified; according to the classification results of the conflict analysis layers, corresponding load scheduling cooperative optimization measures are respectively executed. The application solves the problem of load priority conflict and resource scheduling imbalance in a high load period of the power distribution network, and improves the load scheduling accuracy and the stability of system operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of layered coordination of power distribution networks, and in particular to a layered coordination optimization method and system for power distribution networks. BACKGROUND

[0002] With the large-scale access of renewable energy (such as photovoltaic power generation and wind power generation), the widespread popularity of electric vehicles, and the continuous growth of power demand, the operation environment of power distribution networks is becoming increasingly complex. Not only is the scale of power distribution networks expanding, with a large number of nodes and a wide geographical distribution, but due to the volatility of distributed power sources and user loads, the operation state of power distribution networks exhibits significant dynamic change characteristics. In addition, there are various types of subjects involved in power distribution networks, including power generation enterprises, users, grid operators, etc., and there are diversified target demands among the subjects in the process of operation and dispatching. At the same time, with the development of intelligent devices and monitoring technology, the amount of data generated by power distribution networks has increased dramatically, putting higher demands on the processing capacity of communication networks and computing platforms. With the continuous expansion of the scale of power distribution networks and the increasing complexity of their operation environment, traditional centralized optimization methods face great pressure in dealing with large-scale nodes, rapid dynamic changes, and multi-subject coordination problems. There are differences in operation characteristics and management needs among different regions and levels in power distribution networks, and a single level of optimization method cannot balance the local response efficiency and overall performance of the system. At the same time, due to the frequent fluctuations in the state of distributed power sources and loads, power distribution networks have increasing requirements for real-time, flexibility, and stability. Through layered management, targeted optimization can be performed according to the characteristics of each level to improve the response speed and local autonomy of the system; and through coordination among levels, the overall coordination and optimization effect of the system can be enhanced, thereby better adapting to the complex, changing, and multi-subject involved operation environment of power distribution networks.

[0003] Existing layered coordination optimization techniques for power distribution networks typically logically divide power distribution networks according to different voltage levels, geographical regions, or functional modules, forming a structural system composed of multiple levels such as the main grid, sub-grid, and terminal user network. Through the setting of coordination mechanisms, information is transmitted bidirectionally and optimization instructions are issued between levels. Specifically, the upper level is mainly responsible for global optimization and dispatching and strategy formulation, including load forecasting, power flow optimization, and resource allocation decisions; the middle level focuses on regional coordination and local optimization, handling local power management, node regulation, and state evaluation; and the bottom level focuses on device-level control and execution, such as output regulation of distributed power generation units, energy storage system management, and terminal load response. Through communication networks, each level realizes real-time uploading of state information and timely issuance of control instructions, forming a multi-layered, well-defined optimization control system to cope with multi-scale dynamic changes and multi-objective management needs in the operation of power distribution networks.

[0004] The existing technology has the following deficiencies:

[0005] During the high load period of the power distribution network, especially when the grid load reaches the peak, the load demand of multiple users increases simultaneously, especially when the demand of household, commercial and industrial users is large, the competition for power resources intensifies. In this case, due to the difference in power demand priority between different users, the power distribution network faces the situation of multiple load demand priority conflicts. This priority conflict leads to the fact that the system fails to fully consider the priority of various loads when allocating resources, thereby causing the phenomenon of unbalanced resource scheduling. The hierarchical optimization system of the power distribution network needs to allocate power resources reasonably according to the power demand and priority of the user. However, the existing hierarchical collaborative optimization technology of the power distribution network cannot accurately optimize the scheduling of loads with different priorities according to the conflict degree of the unbalanced resource scheduling phenomenon when multiple load demand priority conflicts occur. Due to this problem, the system fails to accurately allocate appropriate power resources for each user during the peak period, resulting in some loads not being able to be satisfied in time, while other loads consume excessive resources. This scheduling imbalance further causes the instability of the grid load, which may lead to insufficient power supply for some users, and even trigger the power grid protection mechanism, causing power failure. At the same time, the excessive power consumption of low-priority users causes waste of power resources, further reducing the operation efficiency of the power distribution network, increasing the system burden, and affecting the reliability and stability of the power grid.

[0006] The above information disclosed in the BACKGROUND section is only for the purpose of enhancing the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0007] The purpose of the present application is to provide a power distribution network hierarchical collaborative optimization method and system to solve the problems in the background art.

[0008] In order to achieve the above-mentioned purpose, the present application provides the following technical solution: a power distribution network hierarchical collaborative optimization method, specifically comprising the following steps:

[0009] During the high load period of the power distribution network, real-time monitoring of the power demand data and corresponding priority information of each load unit is performed to determine whether there is a load demand priority conflict situation in which load units with different priorities compete for the same power resource at the same time period;

[0010] When it is determined that there is a load demand priority conflict situation, a hierarchical structure of the power distribution network is constructed, the actual power supply information of each load unit in each layer is monitored and analyzed in real time, and the layer that appears the phenomenon of unbalanced resource scheduling is screened and marked as the conflict analysis layer;

[0011] obtain load conflict information of each conflict analysis layer, analyze the load conflict information, evaluate the conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs, and classify the conflict analysis layers;

[0012] According to the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are respectively executed;

[0013] After the load scheduling collaborative optimization measures are executed, the power supply state and resource allocation change of each load unit in each layer are monitored in real time, and the load scheduling strategy is dynamically adjusted according to the real-time monitoring results.

[0014] Preferably, when it is determined that there is a load demand priority conflict, a layered structure of the power distribution network is constructed, specifically: based on the voltage level, geographical area and physical connection relationship of the load unit of the power distribution network, all load units are divided into layers according to the preset layering rule, wherein each layer corresponds to a plurality of load units, and the load unit identifier and real-time power demand data corresponding to each layer are recorded; each layer divides the load units according to the voltage level and geographical area;

[0015] The actual power supply information of each load unit in each layer is monitored and analyzed in real time, and the layers in which the resource scheduling imbalance phenomenon occurs are screened 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 the predetermined threshold value, the layer is determined as a resource scheduling imbalance layer, and is marked as a conflict analysis layer.

[0016] Preferably, the load conflict information of each conflict analysis layer is obtained and analyzed, the conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs is evaluated, and the conflict analysis layers are classified, specifically including the following steps:

[0017] Obtain the load conflict information of each conflict analysis layer, and pre-process the load conflict information after obtaining the load conflict information;

[0018] From the pre-processed load conflict information of each conflict analysis layer, supply-demand dynamic state information and power health evaluation information are extracted, and after the extraction, the supply-demand imbalance index and the power quality index of each conflict analysis layer are respectively generated by analyzing the supply-demand dynamic state information and the power health evaluation information;

[0019] A conflict degree evaluation model is constructed for the generated supply-demand imbalance index and power quality index of each conflict analysis layer, and a conflict coefficient of each conflict analysis layer is generated by weighted summation;

[0020] The preset conflict coefficient threshold interval is determined, and the generated conflict coefficients of each conflict analysis layer are compared with the determined conflict coefficient threshold interval. The conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs is evaluated according to the comparison result, and each conflict analysis layer is divided into a coordination balance layer, a scheduling tension layer and a conflict imbalance layer according to the evaluation result.

[0021] Preferably, the logic for obtaining the supply-demand imbalance index of each conflict analysis layer is as follows:

[0022] The supply-demand dynamic state information is extracted from the preprocessed load conflict information of each conflict analysis layer, specifically including the actual power supply total amount, the total power demand and the power amount fed back to the power distribution network of each load unit in each conflict analysis layer, and is respectively marked as 、 and , represents the actual power supply total amount of the i-th load unit in the j-th conflict analysis layer, represents the total power demand of the i-th load unit in the j-th conflict analysis layer, represents the power amount fed back to the power distribution network of the i-th load unit in the j-th conflict analysis layer, , , and are positive integers. The supply-demand imbalance index of each conflict analysis layer is calculated, and the specific calculation formula is as follows:

[0023] In the formula, j represents the j-th conflict analysis layer, i represents the i-th load unit in the j-th conflict analysis layer, and

[0024]

[0025] In the formula, j represents the j-th conflict analysis layer, i represents the i-th load unit in the j-th conflict analysis layer, and

[0026] Preferably, the logic for obtaining the power quality index of each conflict analysis layer is as follows:

[0027] The power health evaluation information is extracted from the preprocessed load conflict information of each conflict analysis layer, specifically including the rated voltage value, the actual running voltage value and the real-time measured voltage total harmonic distortion rate of each load unit in each conflict analysis layer under normal operation condition, and is respectively marked as 、 and , represents the rated voltage value of the i-th load unit in the j-th conflict analysis layer, represents the actual running voltage value of the i-th load unit in the j-th conflict analysis layer, ​​​​​​​​The rated voltage value that the load unit should maintain under normal operating conditions, represents the actual operating voltage value of the load unit in the conflict analysis layer, represents the real-time measured voltage total harmonic distortion rate of the load unit in the conflict analysis layer, , , and are positive integers;

[0028] The power quality index of each conflict analysis layer is calculated, and the specific calculation formula is as follows:

[0029] ;

[0030] In the formula, is the power quality index of the conflict analysis layer.

[0031] Preferably, the supply-demand imbalance index and the power quality index of each generated conflict analysis layer are used to construct a conflict degree evaluation model, and the conflict coefficient of each conflict analysis layer is generated by weighted summation, and the specific calculation formula is as follows:

[0032] ;

[0033] In the formula, is the conflict coefficient of the conflict analysis layer, and are non-zero weight coefficients of the supply-demand imbalance index and the power quality index of each conflict analysis layer, respectively, and .

[0034] Preferably, a pre-set conflict coefficient threshold interval is determined, and after being determined, it is compared with the conflict coefficient of each generated conflict analysis layer, and according to the comparison result, the conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs is evaluated, and according to the evaluation result, each conflict analysis layer is divided into a coordination and balance layer, a scheduling tense layer and a conflict imbalance layer. The specific comparison analysis and division are as follows:

[0035] If , the conflict degree of the conflict analysis layer when the resource scheduling imbalance phenomenon occurs is low, and the conflict analysis layer is divided into a coordination and balance layer;

[0036] If The conflict degree of the conflict analysis layer in the case of resource scheduling imbalance is moderate conflict degree, and the conflict analysis layer is divided into a scheduling tension layer;

[0037] If The conflict degree of the conflict analysis layer in the case of resource scheduling imbalance is serious conflict degree, and the conflict analysis layer is divided into a conflict imbalance layer.

[0038] Preferably, according to the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are respectively executed, specifically:

[0039] For the conflict analysis layer divided into the coordination balance layer, the load scheduling collaborative optimization measure executed is specifically: continuously monitoring the power demand data and power supply state data of each load unit in the conflict analysis layer in real time, and when it is detected that the power demand change amplitude exceeds the preset change threshold, the resource allocation amount of the corresponding load unit is adjusted to keep the load scheduling strategy consistent with the real-time running state;

[0040] For the conflict analysis layer divided into the scheduling tension layer, the load scheduling collaborative optimization measure executed is specifically: according to the priority level of each load unit in the conflict analysis layer, reducing the power allocation amount of the low-priority load unit and increasing the power allocation amount of the high-priority load unit, and reallocating resources according to the priority control strategy to reduce load competition and improve the supply-demand matching level;

[0041] For the conflict analysis layer divided into the conflict imbalance layer, the load scheduling collaborative optimization measure executed is specifically: starting the standby power supply system associated with the conflict analysis layer, calling the energy storage resource output power configured in the conflict analysis layer, and simultaneously performing load reduction operation on the low-priority load unit, to preferentially guarantee the power supply demand of the high-priority load unit and restore the supply-demand balance state in the conflict analysis layer.

[0042] Preferably, a power distribution network layered collaborative optimization system includes a conflict perception module, a layered identification module, a conflict evaluation module, an optimization execution module, and a dynamic adjustment module;

[0043] The conflict perception module monitors the power demand data and corresponding priority information of each load unit in real time during the high-load period of the power distribution network, and determines whether there is a load demand priority conflict situation in which load units of different priorities compete for the same power resource at the same time period;

[0044] The layered identification module, when it is determined that there is a load demand priority conflict situation, constructs a layered structure of the power distribution network, monitors and analyzes the actual power supply information of each load unit in each layer in real time, screens the layer that appears resource scheduling imbalance, and marks it as a conflict analysis layer;

[0045] The conflict evaluation module obtains the load conflict information of each conflict analysis layer, analyzes the load conflict information, evaluates the conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs, and classifies the conflict degree.

[0046] The optimization execution module executes corresponding load scheduling collaborative optimization measures according to the classification results of each conflict analysis layer.

[0047] The dynamic adjustment module monitors the power supply state and resource allocation change of each load unit in each layer in real time after the load scheduling collaborative optimization measures are executed, and dynamically adjusts the load scheduling strategy according to the real-time monitoring result.

[0048] In the above technical solution, the technical effects and advantages provided by the present application are as follows:

[0049] 1. The present application can accurately identify and respond to resource competition and priority conflict problems between different load units during the high load period of the power distribution network through the hierarchical collaborative optimization method. By monitoring the power demand data and priority information of the load unit in real time, the system can timely find the conflict situation of different priority load units competing for power resources in the same time period, and through the construction of the hierarchical structure and the identification of the resource scheduling imbalance phenomenon, the resource allocation problem is localized and specified, so that the power distribution network can adopt more accurate scheduling strategy. This process effectively improves the load scheduling accuracy of the power distribution network during the high load period, and avoids the low efficiency and imbalance phenomenon caused by the traditional method of scheduling the global resources.

[0050] 2. During the high load period, the priority difference of different load units often leads to power resource allocation conflict. The present application evaluates each conflict analysis layer through the supply-demand imbalance index and the power quality index, quantifies the conflict degree of each layer, and classifies the conflict degree. This enables the system to execute optimization measures according to different conflict degrees, such as adjusting the power consumption allocation of low-priority load units and prioritizing the power supply demand of high-priority load units. Through this differentiated scheduling strategy, the problem of excessive resource consumption by low-priority load units is avoided, and the power supply of high-priority load units is maximized, thereby improving the resource utilization efficiency of the power distribution network, reducing resource waste, and optimizing the balance of load scheduling.

[0051] 3、The application guarantees the stable operation of the power distribution network during the load peak period in a dynamic monitoring and real-time adjustment manner.When resource scheduling is unbalanced, the system can quickly respond by enabling backup power sources, calling energy storage resources, reducing low-priority loads, and other means to quickly restore the supply-demand balance of the system, avoiding power supply interruption and power outage accidents caused by scheduling imbalance.Especially through the dynamic adjustment of each conflict analysis layer, the system can flexibly optimize the load scheduling according to real-time feedback, further improving the anti-disturbance ability and emergency response ability of the power grid, thereby ensuring the stability and reliability of the system in extreme conditions. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0053] Figure 1 A flowchart of the power distribution network layered collaborative optimization method and system of the present application.

[0054] Figure 2 A module diagram of the power distribution network layered collaborative optimization method and system of the present application. DETAILED DESCRIPTION

[0055] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art.

[0056] The present application provides a layered collaborative optimization method for a power distribution network as shown in Figure 1 The present application provides a layered collaborative optimization method for a power distribution network as shown in

[0057] In the high load period of the power distribution network, the power demand data and the corresponding priority information of each load unit are monitored in real time to determine whether there is a load demand priority conflict situation in which load units of different priorities compete for the same power resource in the same time period.

[0058] In order to monitor the power demand data and corresponding priority information of each load unit in real time during the high load period of the power distribution network, a load data collection and processing platform can be built. Specifically, first, the real-time power consumption data of the load unit is uploaded to the power distribution network data center by the existing monitoring equipment such as smart meters and remote terminal units (RTUs) installed in the load unit. Subsequently, a data aggregation and preprocessing module is deployed at the software level, which uses time windows (such as 1 minute or 5 minute granularity) to group process the real-time collected data, and establishes a priority label corresponding to each load unit in the system database (such as preset priority according to user type, contract agreement, and grid dispatching strategy). The data aggregation module associates the power demand data with the priority information in real time to form a complete load unit monitoring data set, thereby realizing real-time and synchronous monitoring of all load units during the high load period and providing basic data support for subsequent conflict identification.

[0059] After the association of the real-time data of the load unit and the priority information is completed, a conflict detection module can be built to analyze the data of all load units in the same region in parallel in time slices (such as every 1 minute). Specifically, first, the power resources are divided into several regional resource pools in the system, each resource pool corresponding to a certain capacity limit. For each time slice, the conflict detection module compares the real-time power demand of each load unit with the remaining allocatable capacity of the regional resource pool, and sorts according to the priority information of the load unit. If in the same resource pool, there are multiple load units of different priorities, the total power demand exceeds the remaining capacity of the resource pool, and there is a high-priority load unit that cannot meet the power demand due to occupation by a low-priority load unit, then it is determined that a load demand priority conflict has occurred. The software system dynamically updates the conflict identification results in each time slice to provide a basis for subsequent hierarchical optimization.

[0060] The reason for real-time monitoring of the power demand and priority information of the load unit and dynamically identifying the priority conflict is that during the high load period of the power distribution network, the power resources themselves are highly scarce and competitive, and the priority difference between the load units directly determines the rationality of resource allocation. If the power demand and priority information are not monitored, the grid dispatching system cannot identify which load units are competing for resources and which load units should be prioritized, which can easily lead to resource occupation by low-priority loads, insufficient power supply for high-priority loads, and ultimately cause local area voltage loss, load imbalance, and even systemic power supply accidents. Through the real-time monitoring and conflict detection mechanism of software, the resource allocation conflicts during the high load period can be perceived and accurately defined in advance, providing accurate input conditions for subsequent hierarchical collaborative optimization based on conflict degree, thereby maximizing the safety, reliability, and power supply fairness of the power grid operation.

[0061] When it is determined that there is a load demand priority conflict, a hierarchical structure of the power distribution network is constructed, actual power supply information of each load unit in each hierarchy is monitored and analyzed in real time, and the hierarchy in which resource scheduling is unbalanced is screened and marked as a conflict analysis layer;

[0062] In this embodiment, when it is determined that there is a load demand priority conflict, a hierarchical structure of the power distribution network is constructed, specifically: based on the voltage level, geographical area and physical connection relationship of the load unit of the power distribution network, all load units are hierarchically divided according to the preset hierarchical rule, wherein each hierarchy corresponds to a plurality of load units, and the load unit identifier and real-time power demand data corresponding to each hierarchy are recorded; each hierarchy divides the load unit according to the voltage level and geographical area;

[0063] When it is determined that there is a load demand priority conflict, the hierarchical structure can be constructed at the software level by constructing a power distribution network hierarchical management module. The specific implementation manner is: first, the basic attribute data of each load unit in the power distribution network is collected through an interface, including the transformer number, voltage level, geographical position code and physical connection relationship information. Subsequently, in the data processing module, data logic division is performed according to the preset hierarchical rule, and the hierarchical rule includes: firstly, the load units are classified according to the voltage level, for example, the load units of different voltage levels such as 110kV, 35kV, 10kV and 0.4kV are classified into different primary hierarchies; in the load units of the same voltage level, the same power supply area and the same substation area are further subdivided into secondary hierarchies. During the division process, the system establishes a load unit index table to correspond each load unit with its hierarchical identifier, forming a hierarchical mapping relationship table. After the hierarchical division, the software system records the identifier information of all load units included in each hierarchy and the corresponding real-time power demand data in real time, and stores them in the hierarchical database in the form of structured data, so as to monitor and analyze the load state of each hierarchy in the subsequent process. The entire hierarchical process is automatically executed, and the hierarchical structure can be updated in real time according to the dynamic changes of the load unit, ensuring the consistency and real-time performance of the system structure.

[0064] The reason for reorganizing the load units of the power distribution network in a hierarchical manner in the case of determining the existence of load demand priority conflicts is mainly that in the high-load and conflict-prone operating environment, the overall power distribution network as a complex system directly optimized in the manner of full-network paving can cause problems such as large data volume, slow response speed, and distorted decision-making. By hierarchical division based on voltage levels, geographic regions, and physical connection relationships, the optimization range can be reasonably limited according to the physical properties and geographic characteristics of each layer to reduce the scale and complexity of optimization processing, and the load units within each layer can be more consistent and controllable in terms of operating characteristics. In this way, subsequent load scheduling optimization and resource allocation adjustment can be performed to develop more accurate and differentiated optimization strategies for different levels of actual supply and demand status, improving the response speed and processing effect of the system on local conflict problems. In addition, the construction of the hierarchical structure enables parallel processing, and the conflict detection and scheduling optimization of each layer can be independently operated, thereby significantly improving the overall scheduling efficiency and system stability, effectively supporting the dynamic collaborative optimization demand in complex load conflict situations.

[0065] The actual power supply information of each load unit in each layer is monitored and analyzed in real time, and the layer with resource scheduling imbalance is selected and marked as a conflict analysis layer. 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, and when the deviation value exceeds the predetermined threshold, the layer is determined as a resource scheduling imbalance layer, and is marked as a conflict analysis layer.

[0066] In real-time monitoring and analyzing the actual power supply information of each load unit in each layer, it can be realized by deploying a hierarchical load monitoring and analyzing module. Specifically, the system first uploads the actual power supply information of each load unit in real time through the intelligent monitoring device of the load unit, including instantaneous power, active power, reactive power and other indicators, and collects and manages them in the database according to the hierarchical structure. For each layer, the data processing module automatically calculates the total actual power supply of all load units in the layer based on a preset time period (e.g. every 5 minutes), and queries the corresponding maximum load demand standard of the layer, which can be obtained through historical data analysis, planning data modeling or operation prediction. Subsequently, the system calculates the resource allocation deviation value of each layer through the difference analysis module, the calculation formula is: (actual power supply total - maximum load demand standard) / maximum load demand standard. The software system monitors the deviation value in real time at each period, and compares it with the preset resource scheduling balance threshold. If the deviation value exceeds the threshold, the system automatically marks the corresponding layer as having resource scheduling imbalance phenomenon, and marks the layer as conflict analysis layer, and enters the subsequent conflict evaluation process. The whole monitoring, calculation and screening process is automatically completed in the data processing platform without manual intervention, realizing efficient and real-time intelligent judgment.

[0067] The reason for real-time collection of actual power supply information of load units in each layer and deviation analysis to screen layers with resource scheduling imbalance is mainly because the load distribution of the distribution network is highly dynamic during high load period or conflict situation, and the real-time power supply of the load unit will fluctuate constantly. If only relying on static setting or preliminary priority judgment, it is difficult to accurately identify those areas where load supply and demand are imbalanced due to unreasonable resource allocation in actual operation, which may lead to serious overload or power shortage in some layers, and further cause power instability in a larger range. Through real-time collection and deviation calculation, not only the current load state of each layer can be dynamically captured, but also whether the resource scheduling of each layer is within a reasonable range can be quantitatively evaluated. When the deviation degree is identified by comparing the maximum load demand standard, the layers with real problems can be quickly screened out, ensuring that the subsequent optimization actions of the system are targeted and accurate, thereby effectively preventing resource waste, power shortage and other problems, and improving the stability and intelligent scheduling capability of the whole distribution network system. This approach not only enhances the real-time responsiveness of the system, but also realizes a unified judgment standard across layers and regions through standardized deviation indicators, providing a reliable foundation for subsequent collaborative optimization.

[0068] Obtain the load conflict information of each conflict analysis layer and analyze it, evaluate the conflict degree of each conflict analysis layer when resource scheduling imbalance occurs, and classify it;

[0069] In this embodiment, the load conflict information of each conflict analysis layer is obtained and analyzed to evaluate the conflict degree of each conflict analysis layer when resource scheduling imbalance occurs, and is classified, specifically including the following steps:

[0070] The load conflict information of each conflict analysis layer is obtained and preprocessed after being obtained;

[0071] In practical application, the load conflict information of each conflict analysis layer can be obtained through a power distribution network data acquisition and management system (such as a distribution automation system DMS, an energy management system EMS). Specifically, first, the data interface module inside the system is used to extract the power supply data, load demand data, voltage real-time monitoring data and power quality index data of each load unit under the hierarchical structure from the real-time database. These data are usually obtained from smart meters, monitoring terminals, transformer terminals (TTU) and power distribution monitoring equipment installed on the load unit side. Then, on the data acquisition server side, the collected data are automatically classified into the corresponding conflict analysis layer according to the pre-set hierarchical identifier, and are simultaneously labeled with a time stamp to ensure the real-time and time sequence consistency of the data. During data acquisition, the system can set a sampling period (such as 1 minute, 5 minutes, etc.), and automatically pull the latest data snapshot in batches within the specified period to form a structured conflict analysis layer load conflict information set, providing complete real-time data support for subsequent analysis and processing.

[0072] The main purpose of preprocessing is to improve the accuracy of subsequent data analysis, improve the efficiency of modeling calculation, and prevent abnormal data from interfering with conflict evaluation results. Since the load conflict information collected from field devices has problems such as noise, missing, repetition, and different time stamps, if not preprocessed, direct analysis and modeling will lead to evaluation deviation or even wrong decision. In the software implementation layer, preprocessing usually includes the following steps: first, data cleaning is performed to eliminate obvious outliers (such as negative power supply, voltage values exceeding the physical range), and missing data is filled by nearest neighbor interpolation or moving average method; second, the data time reference is unified, and all load unit data is ensured to be at the same time by time alignment algorithm (such as resampling based on sampling time window); then, the data fields from different sources are standardized (such as power supply unit unified to kW, voltage standard unified to nominal value percentage), so that the dimensions are consistent for subsequent formula calculation; finally, only the load unit data actually associated within each conflict analysis layer is retained by hierarchical screening logic, and invalid or external interference node data is removed. All preprocessing operations are automatically performed by the data processing module without human intervention, so that the input data of the conflict analysis layer is complete and accurate, and has a unified structure specification, meeting the strict requirements of conflict degree evaluation modeling.

[0073] The supply-demand dynamic state information and the power health evaluation information are extracted from the load conflict information of each conflict analysis layer after preprocessing, and are analyzed after extraction to generate the supply-demand imbalance index and the power quality index of each conflict analysis layer respectively.

[0074] The supply-demand dynamic state information and the power health evaluation information are extracted from the load conflict information of each conflict analysis layer after preprocessing, which can be realized by configuring a feature extraction module in the data processing platform. Specifically, after receiving the conflict analysis layer data after cleaning and standardization processing, the system first screens out the data fields related to the supply-demand dynamic state according to the pre-defined field mapping rules, including the real-time power supply amount, the current load demand amount and the load feedback amount of each load unit, and screens out the data fields related to the power health evaluation, including the real-time voltage value, the standard voltage value and the real-time harmonic content of each load unit. During the extraction process, the system performs batch traversal according to the conflict analysis layer identifier, classifies and collects the data of each hierarchical node, structures the same type of data into a unified format of the supply-demand dynamic state information set and the power health evaluation information set, and automatically marks the corresponding time stamp and level label to ensure the consistency and traceability of the information. To improve the processing efficiency, the system adopts an asynchronous parallel processing strategy to complete the data extraction and classification in multiple conflict analysis layers at the same time, ensuring the independence and integrity of the supply-demand dynamic state information and the power health evaluation information between different conflict analysis layers, thereby laying a solid data foundation for generating the supply-demand imbalance index and the power quality index of each conflict analysis layer.

[0075] A conflict degree evaluation model is constructed for the generated supply-demand imbalance index and power quality index of each conflict analysis layer, and a conflict coefficient of each conflict analysis layer is generated by weighted summation.

[0076] A pre-set conflict coefficient threshold interval is determined, and is compared with the generated conflict coefficient of each conflict analysis layer. According to the comparison result, the conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs is evaluated, and according to the evaluation result, each conflict analysis layer is divided into a coordination balance layer, a scheduling tense layer and a conflict imbalance layer.

[0077] 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.

[0078] In this embodiment, the logic for obtaining the supply and demand imbalance index of each conflict analysis layer is as follows:

[0079] 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;

[0080] 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.

[0081] The supply-demand imbalance index for each conflict analysis layer is calculated using the following formula:

[0082] ;

[0083] In the formula, For the first The supply and demand imbalance index of the conflict analysis layer.

[0084] 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 the total power supply and the total power demand. The purpose of this operation is to correctly 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, thereby giving a more accurate imbalance measure. Next, the introduction of the logarithmic function helps to compress excessively large or small deviation values, avoiding excessive influence of extreme cases on the result, especially when the supply-demand imbalance is slight, subtle changes can be captured sensitively. In actual calculation, the logarithmic operation in the formula makes the system more sensitive to small supply-demand imbalances and appropriately alleviates the calculation impact of large supply-demand differences, making the overall system evaluation more smooth and stable. In addition, the weighted sum averages the supply-demand imbalance of each load unit, making the contribution of each load unit in the conflict analysis layer to the overall evaluation balanced, avoiding excessive influence of some load units due to abnormal data. Finally, through the average value calculation, the imbalance in each conflict analysis layer can be effectively evaluated, ensuring that the evaluation result has high representativeness and practicality, and is suitable for subsequent resource scheduling optimization decisions.

[0085] According to the calculation formula of the supply-demand imbalance index , the numerical value directly reflects the deviation of the overall supply-demand matching degree of the load units in each conflict analysis layer. When the difference between the actual total power supply ( ) and the total power demand ( ) of each load unit is small, and the amount of power fed back to the distribution network ( ) can effectively offset local supply-demand fluctuations, the calculation result of tends to a small value, indicating that the resource scheduling of the conflict analysis layer is balanced, and the conflict degree is slight; on the contrary, when there is a large deviation between the total power supply and the load demand, even if the feedback power is considered, there is still a significant mismatch between supply and demand. After absolute value processing, the imbalance deviation is enlarged, and after logarithmic function compression, will still show a high value, indicating that the resource scheduling of the conflict analysis layer is seriously unbalanced, and the conflict degree is high. Therefore, is smaller, indicating that the supply-demand coordination of the conflict analysis layer is better, the resource scheduling balance is higher, and the conflict degree is slighter; while is larger, indicating that the supply-demand imbalance is more serious, the resource allocation conflict is more prominent, and the conflict degree is more serious, requiring priority for load scheduling optimization and resource reallocation intervention. Through this quantitative relationship, the system can accurately evaluate the conflict severity of each conflict analysis layer in resource scheduling based on the numerical value of , and accordingly divide different optimization priorities.

[0086] In this embodiment, the logic for obtaining the power quality index of each conflict analysis layer is as follows:

[0087] 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;

[0088] 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.

[0089] The power quality index for each conflict analysis layer is calculated using the following formula:

[0090] ;

[0091] In the formula, For the first The power quality index of the conflict analysis layer.

[0092] 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.

[0093] 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.

[0094] 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:

[0095]

[0096] 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 .

[0097] 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.

[0098] 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:

[0099] 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.

[0100] 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.

[0101] If When the resource scheduling imbalance phenomenon occurs, the conflict degree of the conflict analysis layer is a medium conflict degree, and the conflict analysis layer is divided into a scheduling tension layer.

[0102] This case shows that when the resource scheduling imbalance phenomenon occurs, the supply-demand matching in the conflict analysis layer has deviated to a certain extent, the power quality has slightly decreased, the resource competition between the load units has intensified, and the scheduling pressure has increased. At this time, the conflict analysis layer is divided into a scheduling tension layer, which means that targeted scheduling optimization intervention is needed, such as adjusting the load distribution ratio appropriately, and prioritizing the power supply for high-priority load units. The impact is that if not optimized in time, it may gradually evolve into a more serious supply-demand imbalance, increasing the complexity of system scheduling, reducing the flexibility and controllability of overall distribution network operation, and thus adversely affecting local power stability.

[0103] If When the resource scheduling imbalance phenomenon occurs, the conflict degree of the conflict analysis layer is a serious conflict degree, and the conflict analysis layer is divided into a conflict imbalance layer.

[0104] This case shows that when the resource scheduling imbalance phenomenon occurs, there is a serious supply-demand imbalance in the conflict analysis layer, the power quality has deteriorated significantly, the resource allocation between the load units is in sharp conflict, and the system is difficult to maintain operational balance through conventional scheduling means. At this time, the conflict analysis layer is divided into a conflict imbalance layer, which means that immediate mandatory optimization measures must be taken, such as activating backup power sources, mobilizing energy storage resources, and reducing power supply for low-priority loads. The impact is that if not intervened quickly, it is easy to cause local load collapse, power supply interruption, or even trigger distribution network protection actions, thereby posing a serious threat to the safe and stable operation of the entire power grid, causing a decline in power supply reliability and economic losses in a large area.

[0105] According to the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are executed;

[0106] In this embodiment, according to the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are executed, specifically:

[0107] For the conflict analysis layer divided into a coordinated balance layer, the load scheduling collaborative optimization measures executed are specifically: continuously monitoring the power demand data and power supply state data of each load unit in the conflict analysis layer in real time, and when detecting that the power demand change amplitude exceeds a preset change threshold, adjusting the resource allocation of the corresponding load unit to keep the load scheduling strategy consistent with the real-time operating state;

[0108] For the conflict analysis layer divided into the coordination balance layer, the power demand data and power supply state data of each load unit are continuously monitored in real time, and the resource allocation amount is adjusted when the power demand change amplitude exceeds the preset change threshold, which can be realized by the software monitoring module integrated in the load dispatching management platform. Specifically, the system first configures a real-time data access interface for each load unit, periodically collects power demand data (such as instantaneous load, average load) and power supply state data (such as power supply voltage, active power supply amount) through the collection module, and archives them to the hierarchical monitoring database in real time. Subsequently, the data analysis module compares and calculates the change amplitude of the latest collection data of each period with the data of the last reference period, and dynamically compares according to the set change threshold standard. When the power demand change amplitude of a certain load unit exceeds the preset threshold, the system automatically triggers the resource adjustment logic, calls the dispatching control module, increases or reduces the power resource amount allocated to the load unit, and updates the dispatching instruction, to ensure that the overall load dispatching strategy reflects the change in operating state in real time. By continuously monitoring and dynamically adjusting in this way, the problem of overallocation or underallocation of resources caused by small fluctuations of the load unit can be avoided, ensuring that the system can respond quickly at the initial stage of local load change, maintain the dynamic balance of resource supply and demand, further stabilize the overall operating state of the coordination balance layer, reduce the dispatching pressure fluctuation caused by the accumulation of load changes, and improve the dispatching stability and flexibility of the power distribution network system.

[0109] For the conflict analysis layer divided into the dispatching tension layer, the specific load dispatching collaborative optimization measures are: according to the priority level of each load unit in the conflict analysis layer, reducing the power allocation amount of low-priority load units and increasing the power allocation amount of high-priority load units, reallocating resources according to the priority control strategy to reduce load competition and improve the supply-demand matching level;

[0110] For the conflict analysis layer divided into the scheduling stressed layer, according to the priority level of each load unit, the power distribution amount is adjusted and the resources are redistributed, which can be realized through the priority scheduling optimization module in the power distribution network intelligent scheduling control platform. Specifically, the system first calls the priority level information of each load unit in the conflict analysis layer, and sorts the priority of all load units according to the preset priority sorting table. Then, the current power distribution amount of each load unit is read in real time, and it is matched with the priority level. Through the difference analysis logic, it is judged whether the resource allocation conforms to the priority control strategy. When the system detects that the power distribution amount of the low-priority load unit exceeds the level it should get, or the power supply of the high-priority load unit is insufficient, the scheduling module adjusts the power distribution amount of the low-priority load unit according to the priority weight model, releases the resources for the high-priority load unit, and finally redistributes the overall load resources in the conflict analysis layer according to the optimization objective function. Through this dynamic resource redistribution based on priority level, not only can the power supply demand of key load units be prioritized in the scheduling stressed state, but also the scheduling pressure caused by resource competition can be effectively reduced, and the coordination of supply and demand matching of each load unit can be improved, thereby preventing the imbalance between supply and demand from further deteriorating, and maintaining the overall operation stability and resource utilization efficiency of the power distribution network.

[0111] For the conflict analysis layer divided into the conflict imbalance layer, the specific load scheduling collaborative optimization measures are: starting the standby power supply system associated with the conflict analysis layer, calling the output power of the energy storage resources configured in the conflict analysis layer, and performing load reduction operation on the low-priority load unit, to prioritize the power supply demand of the high-priority load unit, and restore the balance between supply and demand in the conflict analysis layer.

[0112] For the conflict analysis layer divided into conflict imbalance layers, the standby power supply system is started, the energy storage resource output power is called, and the load reduction operation is performed on the low-priority load unit, which can be realized through the software control module of the power distribution network emergency response management platform. Specifically, the system first identifies the standby power supply system (such as standby diesel generator set, microgrid system, etc.) and energy storage unit (such as battery energy storage system BESS) associated with each hierarchical node based on the node configuration profile of the conflict analysis layer, and detects the available state in real time through the communication interface. When it is detected that the standby power supply or energy storage system is in an available state and the conflict degree in the conflict analysis layer exceeds the serious threshold, the dispatching module automatically issues an enabling instruction to start the standby power supply and release the energy storage unit output 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 reduction operation on the low-priority load unit, including power limiting, capacity reduction or phased power supply interruption, to ensure that power resources are preferentially supplied to high-priority load units and quickly restore the supply-demand balance of the conflict analysis layer. Through this multi-strategy parallel emergency optimization method, the key load power supply can be maximized in the case of severe resource shortage, preventing the continuous deterioration of supply-demand imbalance from causing a larger range of power grid cascading reactions, significantly improving the emergency resilience and stability of the power distribution network under extreme conflict conditions, and ensuring the overall safe and reliable operation of the system.

[0113] After performing the load dispatching collaborative optimization measures, the power supply state and resource allocation changes of each load unit in each hierarchical layer are monitored in real time, and the load dispatching strategy is dynamically adjusted according to the real-time monitoring results to optimize the load dispatching balance and system operation stability of the power distribution network.

[0114] After performing the load dispatching collaborative optimization measures, the power supply state and resource allocation changes of each load unit in each hierarchical layer are monitored in real time, which can be realized through the load dispatching real-time monitoring module and intelligent analysis engine in the power distribution network comprehensive monitoring platform. Specifically, the load dispatching real-time monitoring module continuously acquires the power supply state information (including actual power supply power, power supply stability index) and resource allocation change information (including the latest resource allocation proportion, change amplitude) of each load unit in each conflict analysis layer through the preset data acquisition interface. After data preprocessing, the intelligent analysis engine performs dynamic trend analysis to identify whether the power supply and resource allocation state of each load unit conforms to the established dispatching strategy in real time. When it is detected that the power supply stability decreases, the allocation proportion is abnormal, or the load supply deviates from the dispatching target, the system triggers the dynamic dispatching adjustment logic according to the analysis result, regenerates the load allocation instruction, and issues it to the execution control layer to realize real-time correction of the resource allocation amount of each load unit, so as to keep the overall dispatching scheme and the actual operation state synchronous matching.

[0115] The reason why the real-time monitoring and dynamic adjustment are continued after the execution of the load scheduling cooperative optimization measures is that the power distribution network operation environment has strong dynamic and uncertainty, and the load demand and supply state can change rapidly due to external factors (such as temperature change, user behavior fluctuation, distributed energy fluctuation, etc.). If the continuous monitoring and timely dynamic adjustment are not performed, the original load scheduling strategy is easy to fail due to the accumulation of actual operation deviation, leading to the worsening of local supply and demand imbalance, and even causing new resource competition and power quality decline. Through real-time monitoring and dynamic adjustment, a rapid response can be performed when the load state is preliminarily deviated, the resource allocation can be timely corrected, the small problem can be prevented from spreading into a systemic risk, the balance of the load scheduling can be continuously optimized, the stability, flexibility and anti-disturbance ability of the overall operation of the power distribution network can be maximized, and thus the core technical goal of the application in the changing environment to ensure the healthy and efficient operation of the power grid is achieved.

[0116] As shown in a power distribution network layered cooperative optimization system shown in the figure, the system comprises a conflict perception module, a layered identification module, a conflict evaluation module, an optimization execution module and a dynamic adjustment module. Figure 2

[0117] The conflict perception module monitors the power supply state of each load unit in each layer in real time during the high load period of the power distribution network, and analyzes the resource allocation of each layer.

[0118] The layered identification module, when it is determined that there is a load demand priority conflict, constructs a layered structure of the power distribution network, monitors and analyzes the actual power supply information of each load unit in each layer, screens the layers with resource scheduling imbalance, and marks them as conflict analysis layers.

[0119] The conflict evaluation module obtains the load conflict information of each conflict analysis layer, analyzes it, evaluates the conflict degree of each conflict analysis layer when the resource scheduling imbalance occurs, and classifies it.

[0120] The optimization execution module executes the corresponding load scheduling cooperative optimization measures according to the classification results of each conflict analysis layer.

[0121] The dynamic adjustment module, after the execution of the load scheduling cooperative optimization measures, monitors the power supply state and resource allocation change of each load unit in each layer in real time, and dynamically adjusts the load scheduling strategy according to the real-time monitoring result.

[0122] The above formulas are dimensionless and the numerical values are calculated. The formula is obtained by collecting a large amount of data to simulate the latest real situation. The preset parameters in the formula are set by the person skilled in the art according to the actual situation. ​

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0128] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0129] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A layered collaborative optimization method for power distribution network, characterized in that, Specifically comprising the following steps: During the high load period of the power distribution network, the power demand data and corresponding priority information of each load unit are monitored in real time to determine whether there is a load demand priority conflict situation in which load units of different priorities compete for the same power resource at the same time period; When it is determined that there is a load demand priority conflict situation, a hierarchical structure of the power distribution network is constructed, and the actual power supply information of each load unit in each hierarchy is monitored and analyzed in real time to screen the hierarchy that has a resource scheduling imbalance and mark it as a conflict analysis layer; When it is determined that there is a load demand priority conflict situation, a hierarchical structure of the power distribution network is constructed, specifically: based on the voltage level, geographical area, and physical connection relationship of the load units of the power distribution network, all load units are hierarchically divided according to the preset hierarchical rules, wherein each hierarchy corresponds to a plurality of load units, and the load unit identifier and real-time power demand data corresponding to each hierarchy are recorded; each hierarchy divides the load units according to the voltage level and geographical area; Obtain the load conflict information of each conflict analysis layer and analyze it to evaluate the conflict degree of each conflict analysis layer when a resource scheduling imbalance occurs, and classify it; Specifically comprising the following steps: Obtain the load conflict information of each conflict analysis layer and preprocess it after obtaining it; From the load conflict information of each conflict analysis layer after preprocessing, extract the supply-demand dynamic state information and power health evaluation information, and analyze them after extraction to generate the supply-demand imbalance index and power quality index of each conflict analysis layer, respectively; 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; Calculate the supply-demand imbalance index of each conflict analysis layer, and the specific calculation formula is as follows: In the formula, is the supply and demand imbalance index of the first conflict analysis layer; 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; Calculate the power quality index of each conflict analysis layer, and the specific calculation formula is as follows: In the formula, is the power quality index of the first conflict analysis layer; A conflict degree evaluation model is constructed for the generated supply-demand imbalance index and power quality index of each conflict analysis layer, and a conflict coefficient of each conflict analysis layer is generated by weighted summation; Determine the pre-set conflict coefficient threshold interval, and compare it with the generated conflict coefficient of each conflict analysis layer after determining it, evaluate the conflict degree of each conflict analysis layer when a resource scheduling imbalance occurs according to the comparison result, and classify each conflict analysis layer into a coordination and balance layer, a scheduling stressed layer, and a conflict imbalance layer according to the evaluation result; According to the classification results of each conflict analysis layer, corresponding load scheduling coordination optimization measures are executed respectively; After executing the load scheduling coordination optimization measures, the power supply state and resource allocation changes of each load unit in each hierarchy are monitored in real time, and the load scheduling strategy is dynamically adjusted according to the real-time monitoring results.

2. The power distribution network layered collaborative optimization method of claim 1, wherein, The actual power supply information of each load unit in each layer is monitored and analyzed in real time, and the layer where resource scheduling imbalance occurs is screened and marked as a conflict analysis layer. 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, and when the deviation value exceeds the predetermined threshold, the layer is determined as a resource scheduling imbalance layer, and is marked as a conflict analysis layer.

3. The power distribution network layered collaborative optimization method of claim 2, wherein, The supply-demand imbalance index of each conflict analysis layer generated and the power quality index A conflict degree evaluation model is constructed, and a conflict coefficient of each conflict analysis layer is generated by weighted summation. The specific calculation formula is as follows: wherein is the conflict coefficient of the th conflict analysis layer, and are the non-zero weight coefficients of the supply-demand imbalance index and the power quality index of each conflict analysis layer, respectively, and .

4. The power distribution network layered collaborative optimization method of claim 3, wherein, determining a preset conflict coefficient threshold interval and after determining, comparing with the conflict coefficients of the generated conflict analysis layers According to the comparison result, the conflict degree of each conflict analysis layer when the resource scheduling imbalance phenomenon occurs is evaluated, and according to the evaluation result, each conflict analysis layer is divided into a coordination balance layer, a scheduling tension layer and a conflict imbalance layer. The specific comparison analysis and division are as follows: If When the resource scheduling imbalance occurs, the conflict degree of the conflict analysis layer is low, and the conflict analysis layer is divided into a coordination balance layer. If In the case of resource scheduling imbalance, the conflict degree of the conflict analysis layer is medium, and the conflict analysis layer is divided into a scheduling tension layer. If In the case of the conflict analysis layer of the resource scheduling imbalance, the conflict degree is a serious conflict degree, and the conflict analysis layer is divided into a conflict imbalance layer.

5. The power distribution network layered collaborative optimization method of claim 4, wherein, According to the classification results of each conflict analysis layer, corresponding load scheduling collaborative optimization measures are respectively executed, specifically: For the conflict analysis layer divided into a coordinated balanced layer, the load scheduling collaborative optimization measures executed are specifically: continuously monitoring the power demand data and power supply state data of each load unit in the conflict analysis layer in real time, and when the detection of the power demand change amplitude exceeds the preset change threshold, the resource allocation of the corresponding load unit is adjusted to keep the load scheduling strategy consistent with the real-time operation state; For the conflict analysis layer divided into a scheduling stressed layer, the load scheduling collaborative optimization measures executed are specifically: according to the priority level of each load unit in the conflict analysis layer, reducing the power allocation of low-priority load units and increasing the power allocation of high-priority load units, and reallocating resources according to the priority control strategy to reduce load competition and improve supply-demand matching level; For the conflict analysis layer divided into a conflict imbalance layer, the load scheduling collaborative optimization measures executed are specifically: starting the standby power supply system associated with the conflict analysis layer, calling the energy storage resource output power configured in the conflict analysis layer, and performing load reduction operation on low-priority load units, to preferentially guarantee the power supply demand of high-priority load units and restore the supply-demand balance state in the conflict analysis layer.

6. A layered collaborative optimization system for power distribution network, which is used to implement the layered collaborative optimization method of any one of claims 1-5, characterized in that, The conflict perception module, the layer identification module, the conflict evaluation module, the optimization execution module, and the dynamic adjustment module are included. The conflict perception module monitors the power demand data and corresponding priority information of each load unit in real time during the high load period of the power distribution network, and determines whether there is a load demand priority conflict situation where different priority load units compete for the same power resource in the same time period. The layer identification module, when it is determined that there is a load demand priority conflict situation, constructs a layer structure of the power distribution network, monitors and analyzes the actual power supply information of each load unit in each layer, screens the layer where resource scheduling imbalance occurs, and marks it as a conflict analysis layer. The conflict evaluation module obtains the load conflict information of each conflict analysis layer and analyzes it, evaluates the conflict degree of each conflict analysis layer when resource scheduling imbalance occurs, and classifies it. The optimization execution module executes corresponding load scheduling collaborative optimization measures according to the classification results of each conflict analysis layer. The dynamic adjustment module monitors the power supply state and resource allocation changes of each load unit in each layer after executing the load scheduling collaborative optimization measures, and dynamically adjusts the load scheduling strategy according to the real-time monitoring results.

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