Power distribution network elastic operation control system based on distributed power supply

Abstract

The invention relates to the technical field of power distribution network control, and discloses a power distribution network flexible operation control system based on a distributed power supply. The system comprises a distributed power supply real-time state monitoring module, a power distribution network elastic state evaluation module and an elastic operation control decision module, the distributed power supply real-time state monitoring module collects operation state data of all accessed distributed power supplies in a power distribution network, and generates a distributed power supply operation state data set after processing; the power distribution network elastic state evaluation module calculates the overall elastic state of the power distribution network according to the data set, grades the overall elastic state, and generates a power distribution network elastic state grade and an associated influence parameter set; the elastic operation control decision module generates an operation control instruction set for different distributed power supplies based on the grade and the parameter set, and performs dynamic operation regulation and control on the power distribution network; the system can comprehensively master the operation state of the distributed power supply, accurately evaluate the elasticity level of the power distribution network, and realize flexible and efficient regulation and control of the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network control, in particular to a power distribution network elastic operation control system based on distributed power supply. BACKGROUND

[0002] With the rapid development of renewable energy technology, the penetration rate of distributed power supply in power distribution networks continues to rise. This change, while optimizing energy structure and reducing carbon emissions, also brings new challenges to the stable operation of power distribution networks. Distributed power supply, such as photovoltaic power generation and wind power generation, is significantly affected by natural conditions and has strong intermittency and volatility. When a large number of distributed power supplies are connected to the power distribution network, it can easily lead to voltage fluctuations, frequency deviations and other problems, and even cause instability in local power grids.

[0003] Traditional power distribution network operation control systems are mostly based on centralized management mode and rely on preset operation schemes and fixed control strategies, making it difficult to respond to the dynamic changes of distributed power supply in real time. In the event of extreme weather, equipment failure and other emergencies, the anti-interference ability and rapid recovery ability of the power distribution network are insufficient, often causing large-scale power outages and affecting normal power consumption and social and economic activities of users.

[0004] The existing power distribution network does not comprehensively monitor the state of distributed power supply, and the data processing method is relatively single, making it difficult to form an effective data set to support elastic state evaluation. The lack of elastic state evaluation method also leads to the inability to accurately determine the elasticity level of the power distribution network under different operating conditions, thereby affecting the scientificity and timeliness of control decisions. The existence of these problems makes it difficult for the power distribution network to achieve efficient and flexible operation regulation when dealing with complex working conditions caused by the connection of distributed power supply, which restricts the further promotion and application of distributed energy. SUMMARY

[0005] To solve the above technical problems, the present application provides a power distribution network elastic operation control system based on distributed power supply.

[0006] The technical scheme adopted by the present application is: a power distribution network elastic operation control system based on distributed power supply, comprising:

[0007] a distributed power supply real-time state monitoring module for collecting the operating state data of all connected distributed power supplies in the power distribution network and performing standardized processing on the operating state data to generate a distributed power supply operating state data set;

[0008] a power distribution network elastic state evaluation module for calculating and grading the overall elastic state of the power distribution network according to the distributed power supply operating state data set, generating a power distribution network elastic state level and associated influence parameter set;

[0009] The elastic operation control decision module is configured to generate a set of operation control instructions for different distributed power sources based on the power distribution network elastic state level and the associated set of impact parameters, and to dynamically regulate the operation of the power distribution network according to the set of operation control instructions.

[0010] Further, the distributed power source real-time state monitoring module identifies an abnormal state type in the operation state data of the distributed power source by acquiring the operation state data of the distributed power source.

[0011] The identified abnormal state type is subjected to state feature coding to acquire a state feature identification code, and the state feature identification code is compared with a known sample state type recorded in a state feature identification code mapping table.

[0012] When there is no sample state type matching the abnormal state type in the state feature identification code mapping table, the state feature identification code is initialized as a specific numerical value.

[0013] When there is a sample state type matching the abnormal state type in the state feature identification code mapping table, the state feature identification code corresponding to the matching sample state type is associated with the abnormal state type.

[0014] Further, the distributed power source real-time state monitoring module locates the region to which the associated distributed power source belongs according to the state feature identification code that is not the specific numerical value, and updates the abnormal state count corresponding to the region.

[0015] The region identifier, topological position information, state feature identification code, and abnormal occurrence time of the abnormal state distributed power source are extracted, sorted and combined to generate a region operation state sequence.

[0016] According to the region identifier in the region operation state sequence, the region operation state atlas in the power distribution network elastic state evaluation module is updated. The region operation state atlas is a graphical data model for power distribution network elasticity analysis, which includes the physical connection topology of the power distribution network and superimposes a real-time operation state layer. The atlas nodes are divided into two categories: the bottom nodes map the actual electrical nodes, which represent the real-time collected data; and the upper nodes represent the logical regions, which represent the data aggregated from the bottom nodes in the jurisdiction area. The node attributes include the number of normal and abnormal distributed power sources currently connected to the node, the load level, and the voltage state. The edges in the atlas are used to represent the connection relationship. Among them, the edges representing the lines have attributes including the load rate, and the edges representing the switches have attributes including the on-off state.

[0017] Further, the power distribution network elastic state evaluation module triggers an evaluation instruction when the region operation state atlas is updated.

[0018] The updated region is marked as a target evaluation region.

[0019] The total number of distributed power sources accessed in the statistical target evaluation area is evaluated, and the number of distributed power sources in abnormal state is counted;

[0020] Data is integrated through the regional elasticity evaluation calculation rule, and the elasticity state identifier corresponding to the target evaluation area is output.

[0021] Further, the power distribution network elasticity state evaluation module includes an elasticity state level mapping module, which determines the elasticity state level corresponding to the target evaluation area according to the value of the elasticity state identifier;

[0022] When the elasticity state identifier is a specific value, it is determined that the elasticity state of the target evaluation area is normal;

[0023] When the elasticity state identifier is a non-specific value, a deep evaluation instruction is triggered, and secondary data integration is performed through the elasticity impact degree calculation rule, and the elasticity impact level identifier corresponding to the target evaluation area is output, including a first level and a second level.

[0024] Further, the elasticity state level mapping module marks the target evaluation area corresponding to the elasticity impact level identifier with a value of the first level as a low elasticity impact area;

[0025] The target evaluation area corresponding to the elasticity impact level identifier with a value of the second level is marked as a high elasticity impact area;

[0026] The elasticity operation control decision module generates corresponding operation control instruction set according to the marking of the low elasticity impact area or the high elasticity impact area.

[0027] Further, the power distribution network elasticity state evaluation module obtains all state characteristic identifier codes of non-specific values;

[0028] The occurrence frequency and the sum of values of different state characteristic identifier codes are counted;

[0029] The type impact value corresponding to different abnormal state types is calculated through the state type impact analysis rule.

[0030] Further, the elasticity operation control decision module maintains the original operation control strategy for the abnormal state type with a type impact value of a specific state value, and generates an operation control strategy optimization instruction for the abnormal state type with a type impact value of a non-specific state value, wherein the specific state value is a type impact threshold value preset by the system.

[0031] Further, the elasticity operation control decision module includes a control instruction verification unit, which performs power distribution network operation state deduction according to the operation control instruction set, and verifies the validity of the operation control instruction set according to the deduction result;

[0032] When the verification fails, a command adjustment signal is generated and fed back to the flexible operation control decision module for iteration of the operation control instruction set.

[0033] Further, the system further comprises:

[0034] The permission management module is configured to configure the operation permission levels of different users on the distributed power source real-time state monitoring module, the power distribution network flexible state assessment module, and the flexible operation control decision module.

[0035] The system operation monitoring module is configured to monitor the operation state data flow of the distributed power source real-time state monitoring module, the power distribution network flexible state assessment module, and the flexible operation control decision module in real time.

[0036] The present application has the following beneficial effects relative to the prior art:

[0037] The distributed power source real-time state monitoring module collects and standardizes the operation state data of all distributed power sources connected to the power distribution network, and the generated distributed power source operation state data set can comprehensively and accurately reflect the real-time operation state of the distributed power source, providing reliable basic information for subsequent power distribution network flexible state assessment. This comprehensive data collection and processing method avoids assessment deviation caused by missing data or inconsistent formats, making the judgment of the flexible state of the power distribution network more objective.

[0038] The power distribution network flexible state assessment module calculates and classifies the overall flexible state based on the distributed power source operation state data set, and the generated flexible state level and associated impact parameter set can clearly present the current flexible level of the power distribution network and the key factors affecting the flexibility. This process breaks the traditional fuzzy situation of recognizing the flexible state in the operation of the power distribution network, allowing the operation and management personnel to intuitively understand the flexible performance of the power distribution network under different working conditions and identify potential factors that may affect the stable operation of the power distribution network, thereby more targetedly carrying out subsequent control work.

[0039] The flexible operation control decision module generates a control instruction set based on the power distribution network flexible state level and the associated impact parameter set, and dynamically regulates the power distribution network accordingly, achieving fine management of the distributed power source. This dynamic regulation method can adjust the control strategy in real time according to the changes in the flexible state of the power distribution network, adapt to the intermittent and volatile characteristics of the distributed power source, and enable the power distribution network to maintain a relatively stable operation state under various complex working conditions. At the same time, the differentiated control instructions for different distributed power sources avoid the resource waste or insufficient regulation caused by one-size-fits-all control, improving the economy and reliability of the operation of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0040] The present application will be further described below in conjunction with the accompanying drawings:

[0041] Figure 1 A timing diagram of the power distribution network elastic operation control system based on the distributed power supply provided by the embodiment of the present application;

[0042] Figure 2 A flowchart for abnormal state type identification and coding;

[0043] Figure 3 A flowchart for regional operation state sequence generation and atlas updating;

[0044] Figure 4 A flowchart for elastic state level determination and depth evaluation;

[0045] Figure 5 A flowchart for abnormal state type influence analysis. DETAILED DESCRIPTION

[0046] As shown in Figures 1 to 5 , the present application provides a power distribution network elastic operation control system based on distributed power supply, mainly comprising: a distributed power supply real-time state monitoring module, a power distribution network elastic state evaluation module, and an elastic operation control decision module.

[0047] The distributed power supply real-time state monitoring module is responsible for collecting the operation state data of all the distributed power supplies connected to the power distribution network, including voltage, current, power output, equipment temperature, and grid-connected point state information. The module standardizes the collected raw data, eliminates dimensional differences, unifies the data format, and generates a distributed power supply operation state data set. The power distribution network elastic state evaluation module receives the above data set and calculates the overall elastic state of the power distribution network. The calculation process is based on a predefined elastic index algorithm, integrates the available capacity, response rate of the distributed power supply, and network topology relationship, and outputs an elastic state quantitative value. The quantitative value is divided into different levels according to the threshold range, and an associated influence parameter set is generated, including the affected area identifier, the elastic decline degree, and the expected recovery time. The elastic operation control decision module generates a set of operation control instructions based on the elastic state level and the influence parameter set. The operation control instruction set includes power adjustment, start-stop control, and operation mode switching commands for different types of distributed power supplies. The module issues instructions through the power distribution network energy management system interface to realize real-time regulation and control of the distributed power supply and dynamically adjust the operation state of the power distribution network.

[0048] Embodiment 1: Referring to Figure 2 , the distributed power supply real-time state monitoring module identifies the abnormal state type in the operation state data through the obtained operation state data of the distributed power supply, and encodes the identified abnormal state type to obtain the corresponding state feature identifier code;

[0049] When there is no sample state type matching the abnormal state type in the state feature identification code mapping table, the state feature identification code is initialized to a specific value;

[0050] When there is a sample state type matching the abnormal state type in the state feature identification code mapping table, the state feature identification code corresponding to the matched sample state type is associated to the abnormal state type.

[0051] In the actual operation environment of the power distribution network, the implementation of the real-time state monitoring module of the distributed power supply is a continuous and dynamic process. The module collects the operating state data of the distributed power supply at a high frequency through a group of sensors, smart meters, and communication units deployed at various types of distributed power supply grid-connected points and key nodes. These data include, but are not limited to, the DC side voltage of the photovoltaic inverter, the AC output current, the active and reactive power, the frequency offset, the internal radiator temperature of the inverter, and the speed, pitch angle, generator winding temperature, and converter state signal of the wind turbine. All data are transmitted to the built-in data concentrator of the module through power line carrier communication, wireless private network, or optical fiber network for preliminary data cleaning and time stamp alignment.

[0052] Due to the differences in data format, sampling period, and dimension of devices from different sources, the built-in processor of the module will perform a series of conversion operations. For example, all voltage values are converted to per unit values based on the system rated voltage; current values are converted to amperes; power values are converted to megawatts; temperature values are converted to degrees Celsius; and time information is unified to the coordinated universal time format. This process generates a structured and time-consistent distributed power supply operating state data set, providing standardized input for state recognition.

[0053] The recognition process is not a simple one-to-one threshold comparison, but a rule engine with multi-parameter correlation. The engine has built-in rich judgment logic, for example, not all cases of exceeding rated voltage by 105% are immediately classified as "overvoltage anomaly", the system will simultaneously check whether the current of the node is abnormally increased synchronously, whether the reactive power compensation device in the area is acting, and whether the voltage of the adjacent node is also rising. This correlation analysis can effectively distinguish between global voltage fluctuations and local device faults. The recognized abnormal state types are assigned clear classification labels, such as "persistent overvoltage", "transient frequency limit", "communication interruption accompanied by power zero", etc. Each type of recognized type corresponds to different physical meaning and potential impact. And these text descriptions of abnormal state types are converted into standard codes that can be efficiently processed and compared by machines as state feature encodings. The coding rules use a hierarchical structure, for example, the code "OV-01-SEV2" may represent the first sub-type of overvoltage class anomaly, and the severity is level two. This encoding not only compresses the data volume, but also establishes a standardized description system for abnormal state types. The encoded state feature identification code is convenient for storage, transmission and quick retrieval.

[0054] The state feature identification code mapping table is a dynamically updated knowledge base stored in the non-volatile memory of the system. This table records all the abnormal state type samples that have been recognized and processed by the system in history, as well as the unique identification code assigned to them. When a new abnormal state is recognized and encoded, the system will automatically query and match in the mapping table. The matching process is not a simple string comparison, but a comparison of the core feature parameters of the anomaly, such as voltage deviation percentage, duration, location topology characteristics, etc. If there is no sample record in the mapping table that matches the current abnormal state feature identification code, it means that this may be a new abnormal state type that has never been encountered or has significantly different features. The system will mark the state feature identification code generated this time as an initial state, usually identified by a reserved specific value (such as "0" or "NULL"). This marking means that this abnormal state type needs special attention and further analysis by the subsequent modules, and its encoding will be temporarily stored, waiting for more data to confirm.

[0055] Conversely, if a highly matching sample record is found in the mapping table, the system performs a correlation operation. It associates the current abnormal instance with the existing sample state type in the mapping table, and directly adopts the state feature identification code corresponding to the sample state type that has been verified. For example, if the current identified overvoltage abnormality has a high degree of coincidence with the 15th sample record recorded in the mapping table, the system will mark the current abnormal instance as a known "OV-15" type, and adopt its corresponding complete identification code. This process ensures the consistency of the system's response to similar abnormalities, avoids assigning different codes for the same essential problem, and enables subsequent statistical analysis and control decision-making to be based on stable and reliable data classification.

[0056] Embodiment 2: see Figure 3 During the operation of the power distribution network, the distributed power source real-time state monitoring module continuously generates a large amount of data stream with state feature identification codes. After the module identifies an abnormality and generates an identification code, the key to subsequent processing is to associate these discrete abnormal events with the actual physical structure of the power distribution network, and convert them into regionalized state information that can be used by advanced application modules. This process begins with the screening of identification codes. The system data processing unit filters out all non-specific numerical state feature identification codes, which represent abnormal state instances that have been confirmed and require further processing.

[0057] Each valid state feature identification code is associated with a specific distributed power source device, and the system obtains the detailed access information of the device by querying the distributed power source registration database. This database records the unique code of each distributed power source, the name of the substation it accesses, the feeder number, the management unit it belongs to, and the higher-level power supply area division. Based on this, the system can accurately locate the abnormal device to its corresponding electrical area. This area is a logical concept, which may correspond to the range supplied by a power distribution feeder, or a power supply grid divided by a tie switch. After positioning, the system updates the abnormal state count value corresponding to the area. This count value is a basic indicator of the area's health status, and an increase in its value means that the operational disturbances in the area are accumulating.

[0058] At the same time, the system extracts deep information related to the abnormal event from multiple data sources, including the area identification of the distributed power source device, which is a unique geographical or logical partition code; its topological location information, which describes the specific location of the device in the power distribution network structure, such as which node of which feeder it is connected to, its connection relationship with adjacent switches, loads, and other power sources; the complete state feature identification code, which accurately describes the type and nature of the abnormality; and the timestamp of the abnormality occurrence, which records the exact time of the abnormal event. These data are considered as a complete information package.

[0059] In order to perform efficient time series analysis and region comparison, the system does not process these information packages separately, but sorts and combines them according to predefined rules. The primary basis for sorting is the region identifier, and all abnormal events occurring in the same region are grouped together. Secondly, within the region, the abnormal events are arranged in chronological order. This sorting and combination generates a structured sequence of region operating states. Each sequence corresponds to a region, and each record in the sequence details a specific abnormality occurring at a specific time on a specific device. This sequence dynamically reflects the evolution of the region's operating state and is a data queue that is constantly updated over time.

[0060] The generation of regional operation state sequence triggers a deeper data fusion process. The system updates the detailed information contained in the sequence to the regional operation state atlas in the distribution network resilience state assessment module according to the regional identifier in the sequence. The atlas is a graphical data model specially designed for distribution network resilience analysis. It not only contains the physical connection topology of the distribution network, but also superimposes the real-time operation state layer. The atlas nodes are divided into two categories: the bottom layer nodes map the actual electrical nodes (such as access points), which represent real-time collected data; the upper layer nodes represent logical regions, which represent data aggregated from the bottom layer nodes in the jurisdiction area. The node attributes include the number of normal and abnormal distributed power equipment connected to the node, load level, voltage state, etc. The edges in the atlas represent the connection relationship; among them, the edges representing the lines have attributes including load rate, and the edges representing the switches have attributes including on-off state. Specifically, after the generation of the regional operation state sequence, the system accurately locates the corresponding upper layer logical region node in the regional operation state atlas according to the explicit regional identifier in the sequence. The system iterates through each abnormal state record in the sequence, associates the specific electrical nodes (such as the access points of distributed power) in the bottom layer structure of the atlas according to the detailed topological location information in the record, and directly updates the real-time operation state attributes of these bottom layer nodes, including setting the abnormal state flag, refreshing the real-time collected data such as voltage and power. On this basis, the system performs data aggregation operation, accumulates the number of distributed power in abnormal state of all bottom layer nodes under the jurisdiction of the upper layer logical region node, and synchronously calculates the total number of normal distributed power, the current total load level, and the regional average voltage state based on the integration of bottom layer node data, and then updates the comprehensive attribute set of the upper layer region node. At the same time, the system processes the edges representing the connection relationship in the atlas in parallel: for the edges representing the distribution lines, the load rate attribute is updated according to the real-time monitoring power flow data; for the edges representing the switch devices, the on-off state attribute is refreshed according to the switch position signal received from the distribution automation system. This series of operations realizes the deep integration of discrete regional operation state sequence information into structured regional operation state atlas, completes the fusion of real-time operation state layer and static physical topology layer, and provides an immediate, accurate, and topologically related data model basis for the distribution network resilience state assessment module.

[0061] When a new regional operation state sequence arrives, the atlas update algorithm locates the region node corresponding to the sequence and modifies its attributes. For example, the abnormal device count of the region is increased, a new event record is appended to the "abnormal event list" of the node, and the "state flag" of the node may be adjusted according to the abnormal type (such as from "normal" to "warning"). This process enables the originally abstract distribution network topology graph to be endowed with real-time, dynamic operation state semantics, transforming it into a real "state" atlas.

[0062] The power distribution network resilience state evaluation module monitors the changes of the regional operation state atlas. The attribute update of any atlas node will automatically trigger the evaluation instruction inside the module. The system will mark the region that has been updated as the target evaluation region of this round of evaluation. After the evaluation instruction is started, the evaluation algorithm first performs data statistics: the total number of distributed power supply equipment connected in the target evaluation region is obtained from the updated atlas, which is a relatively static value and comes from the power grid planning data; at the same time, the number of distributed power supply equipment in the abnormal state in the region is obtained, which is a dynamic value and directly comes from the just-updated atlas node attribute.

[0063] The regional resilience evaluation calculation rule is called to integrate and analyze the above data. The rule is not a simple proportional calculation, but an evaluation function that comprehensively considers multiple factors. The input of the rule includes not only the ratio of the number of abnormal devices to the total number, but also the type weight of the abnormal devices (for example, the failure of a megawatt photovoltaic power station has different effects from the failure of a kilowatt rooftop photovoltaic), the average duration of the abnormality, and the importance coefficient of the region in the power distribution network structure. Through the calculation of this rule, the system outputs a quantitative result, i.e., the resilience state identifier corresponding to the target evaluation region. This resilience state identifier is a comprehensive index, and the numerical value directly reflects the ability of the region to withstand disturbances and maintain normal power supply at the current time, providing the most direct numerical basis for subsequent level division and control decision-making.

[0064] Embodiment 3: refer to Figure 4 The resilience state level mapping module built in the power distribution network resilience state evaluation module is responsible for converting the resilience state identifier value calculated into a resilience state level with clear operational guidance significance. The module maintains a resilience level division table, which defines the level corresponding to the resilience state identifier in different numerical intervals. For example, when the numerical value of the resilience state identifier falls in the interval [0, 0.2), it is determined that the resilience state of the target evaluation region is normal; when the numerical value falls in the interval [0.2, 0.5), it is determined to be in the warning level; and when the numerical value falls in the interval [0.5, 1.0], it is determined to be in the fault level. The core of the determination logic is to check whether the numerical value of the resilience state identifier is equal to a specific value, which is defined as “0” representing the absolute normal state during system initialization. When the resilience state identifier is equal to this specific value, it means that there is no abnormal state in the target evaluation region, or the influence of all abnormal states can be ignored, so the module determines that the resilience state of the region is normal and ends the current evaluation period of the region.

[0065] When the value of the elasticity state identifier is not equal to the specific value, it indicates that there is a clear decrease in elasticity in the target evaluation area, and the module triggers a deep evaluation instruction. The deep evaluation process calls an elasticity impact degree calculation rule, which performs secondary integration and analysis on the data in the region operation state atlas. The factors considered in the secondary integration are more detailed and comprehensive than those in the preliminary evaluation, and the input data includes but is not limited to: the type distribution of the current abnormal state (i.e., the statistical results of different state characteristic identifier codes), the duration of each type of abnormality, the proportion of the total installed capacity of abnormal devices to the total capacity of distributed power sources in the region, the current load level of the region, and the connection state of the region to the main grid (whether it has the condition for island operation), etc.

[0066] The elasticity impact degree calculation rule processes these multi-dimensional data through a comprehensive weighting algorithm, and finally outputs a more detailed quantitative indicator, i.e., the elasticity impact level identifier . The calculation method is as follows:

[0067] ;

[0068] Among them: represents the calculated elasticity impact level identifier, which is a continuous value between 0 and 1; represents the total rated capacity of distributed power sources in the region that are in abnormal state; represents the total rated capacity of all distributed power sources in the region; represents the number of abnormal state types in the region; represents the average duration of the th abnormal state type from occurrence to the current time; represents the preset impact weight coefficient of the th abnormal state type, which is determined based on historical data analysis and expert knowledge, and is used to distinguish the potential threat degree of different types of abnormalities to system stability; represents the maximum duration threshold of the system allowed to be abnormal; represents the self-consistent operation ability coefficient of the region, which is an index calculated according to the balance ability of power supply and load, the configuration of energy storage, and the completeness of island control strategy in the region, and the value is between 0 and 1, 1 represents complete self-consistent ability; , , are capacity ratio factor, time impact factor and self-consistent ability factor respectively, which are three weighting coefficients satisfying , and their specific values are determined through system debugging and simulation, and are used to adjust the contribution proportion of each factor in the overall evaluation.

[0069] The elastic state level mapping module receives the calculated The value is then used to determine its level based on another set of level mapping tables. This level mapping table will... The numerical range is divided into two main levels of influence. For example, when When the value falls within the range of [0.2, 0.6), its corresponding elasticity impact level is determined to be Level 1; when When the value falls within the range of [0.6, 1.0], it is classified as Level 2. The module then marks the target assessment area corresponding to the Level 1 elasticity impact indicator as a low elasticity impact area. This marking indicates that although there is a decrease in elasticity in this area, the degree is relatively mild, and the overall system operational stability is not fundamentally threatened. It may manifest as slight local voltage fluctuations or a small power deficit, but it still possesses a strong ability to self-regulate and maintain operation. Conversely, the module marks the target assessment area corresponding to the Level 2 elasticity impact indicator as a high elasticity impact area. This marking indicates that this area is experiencing severe elasticity decay, and the system operational stability faces a significant risk. It may manifest as severe voltage exceedances, frequency instability, or a large power deficit, requiring external intervention to prevent the situation from worsening.

[0070] The flexible operation control decision module monitors the marking status of each area in real time. Once it obtains the marking information of a low-elasticity impact area or a high-elasticity impact area, it immediately initiates the corresponding control command generation process. For targets marked as low-elasticity impact areas, the flexible operation control decision module retrieves the pre-set mild adjustment strategies in its strategy library. The generated operation control command set may include: adjusting the reactive power output of normally operating distributed power sources in the area to support voltage, slightly increasing or decreasing their active power to balance local power, switching reactive power compensation devices in the area, or requesting slight power support from adjacent areas. These commands aim to suppress the trend of decreased elasticity through fine-tuning, bringing the area's operating status back to the normal range.

[0071] For targets marked as high-elasticity impact areas, the elasticity operation control decision module will take more decisive and forceful control measures, and the generated operation control instruction set will be more complex and comprehensive, possibly including: emergency start-stop of backup distributed generator sets or energy storage systems in the area, execution of load shedding operations for non-critical loads, changes to network topology (such as closing tie switches to transfer loads), or even starting an island operation mode when conditions permit, isolating the area from the main grid and running independently on internal power sources. The generation process of the instruction set will fully consider the coupling relationship and execution timing between various control actions to avoid creating new instability factors. All generated instruction sets are ultimately converted into specific, identifiable commands for lower-level execution units and issued to corresponding circuit breakers, power generation controllers, energy storage converters, and other devices through a communication network for execution, thereby completing dynamic regulation and control of power distribution network operation to respond to elasticity decline events.

[0072] Embodiment 4: Referring to Figure 5 The power distribution network elasticity state evaluation module continuously receives data streams from the distributed power source real-time state monitoring module, which contains a large number of abnormal state instances marked by state characteristic identification codes. These identification codes are standardized codes that accurately describe the type and nature of the abnormality. The processing logic of the module is not limited to responding to individual events, but rather aims to identify the distribution pattern of abnormal types and their potential impact patterns from a macro perspective, thereby providing directional guidance for optimizing control strategies.

[0073] The module first filters all incoming state characteristic identification codes, filtering out those with specific numerical values (these codes represent unknown or initialized abnormal types that need to be observed), and focuses on processing those that have been identified and classified into known types (known type identification codes exist in the form of numerical codes, used to explicitly identify various types of abnormalities recognized by the system). Subsequently, the system starts a statistical aggregation process. This process uses each unique state characteristic identification code (i.e., a specific identification code) as a key value to perform two statistical calculations: one is to count the frequency of the specific identification code appearing within a set statistical time window; the second is to calculate the sum of the numerical values of these appearing specific identification codes. It is important to note that the "numerical sum" here does not refer to the character value of the identification code itself, but rather to an internal weight value associated with each identification code in the system. This weight value is assigned when the identification code is defined, and is used to initially quantify the severity of the abnormal type. For example, code "FREQ-001" may have a weight value of 5, while code "VOLT-003" may have a weight value of 8.

[0074] After the statistics are completed, the system obtains a basic data set for each type of known abnormal state, including the number of occurrences and the cumulative sum of its weight value. Then the state type impact analysis rule is called to deeply integrate and analyze the data. The rule is a multi-factor decision logic, and its analysis process not only depends on simple frequency and weight accumulation, but also introduces dynamic parameters such as time decay factor and space distribution factor. The time decay factor gives higher weight to recent abnormal events, considering that they can better reflect the current system state; the space distribution factor assesses whether the type of abnormality is concentrated in a vulnerable area or widely dispersed throughout the network, the former usually means higher local risk. By weighting these dimensions, the rule outputs a final quantitative type impact value for each type of abnormal state. This value is a comprehensive indicator reflecting the overall impact of the type of abnormality on the overall resilience of the distribution network within a certain period of time.

[0075] The system internally sets a type impact threshold, which is a pre-calculated and set reference value. The threshold divides the abnormal state types into two processing categories. The resilience operation control decision module receives the type impact value data stream from the distribution network resilience state assessment module in real time. For abnormal state types with type impact values equal to or lower than the specific state value, the resilience operation control decision module determines that they are currently in an acceptable range or have been effectively suppressed by existing control strategies. The specific state value refers to the type impact threshold mentioned above. For these types, the resilience operation control decision module maintains its original operation control strategy and does not initiate any strategy change instructions. The existing control logic, which may be the previously optimized strategy for this type of abnormality, is considered sufficient and effective, and continues to be executed.

[0076] On the contrary, for abnormal state types with type impact values that are not specific state values (i.e., higher than the type impact threshold), the resilience operation control decision module determines that the impact of this type of abnormality is expanding or that the existing control strategy is ineffective. The resilience operation control module then generates an operation control strategy optimization instruction. The instruction is a trigger signal that does not directly contain specific strategy content, but indicates the target abnormal type that needs to be optimized and its current impact degree estimate. This instruction is sent to the strategy optimization engine.

[0077] The policy optimization engine initiates a policy reconstruction process according to the instructions. The process first queries the historical database to retrieve all historical event records, executed control instructions, and their effectiveness evaluation reports related to the abnormal type. Then, the engine may generate one or more alternative optimization policy schemes using rule-based reasoning or case matching methods. These schemes may include adjusting the response threshold for this type of abnormality, modifying the parameters of control actions (such as increasing the power adjustment amount, shortening the response delay), introducing new collaborative control objects (such as linking energy storage systems or reactive power compensation devices), or in extreme cases, triggering higher-level system protection logic. After simulation and effectiveness verification, the generated optimization policy scheme is updated to the policy library, replacing or supplementing the original policy, thus completing the iterative optimization of the running control policy.

[0078] Table 1: Abnormal state type influence analysis table.

[0079]

[0080] Referring to Table 1, the table shows the analysis results of the system for five different abnormal state types. For example, the overvoltage type abnormality with identification code "VOLT-003" has a frequency of 27 times, a cumulative weight of 216, a high time decay coefficient indicating recent frequent occurrence, and a very high spatial concentration index indicating that the abnormality is concentrated in a certain area. The final calculated type influence value far exceeds the threshold, so the system's handling suggestion is "optimize the policy". On the contrary, the "FREQ-001" type abnormality has a higher frequency, but its spatial distribution is relatively dispersed, and the comprehensive influence value is below the threshold, so the suggestion is "maintain the policy". This data-based analysis process makes the adjustment of the control policy have a clear target and pertinence.

[0081] Example 5: The elastic operation control decision module is internally integrated with a control instruction verification unit, which undertakes the key responsibility of safety and effectiveness verification before the actual issuance and execution of instructions. The workflow of this unit starts with receiving the set of operation control instructions generated by the decision logic. These instruction sets usually contain a series of specific control commands, such as adjusting the active / reactive output set value of a specific distributed power source, opening or closing certain circuit breakers, switching the charge / discharge mode of energy storage systems, or starting an island operation sequence, etc. The instruction set also includes the expected time window and sequence of executing these commands.

[0082] The control instruction verification unit can start the power distribution network operation state deduction process. The deduction is not a simple logical check, but a power distribution network operation state deduction based on a highly realistic power distribution network real-time digital twin model, which refers to a digital model that can real-time synchronize the actual physical structure, equipment parameters and operation state of the power distribution network. The model continuously receives and synchronizes the topology structure, equipment parameters, current operating point (node voltage, line flow, load level, distributed power output state) and other data of the actual power distribution network, and constructs a virtual mirror consistent with the actual power distribution network operation state, providing a highly consistent simulation environment for the actual power distribution network for subsequent simulation of control instruction execution effect. And through the deduction engine, the set of operation control instructions to be verified is injected into this model to simulate the dynamic response process after the instructions are executed in the real power distribution network environment. The deduction engine is the core functional unit of the power distribution network real-time digital twin model for simulating the execution process of control instructions. Its role is to input the set of operation control instructions to be verified (such as adjusting the output of distributed power, switching the state of the switch, etc.) into the digital twin model, simulate the change process of key parameters such as node voltage, line current, system frequency, etc. when these instructions are executed in the real power distribution network according to the physical operation rules of the power distribution network (such as the rules of power flow calculation, equipment start-stop logic, etc.), while following the dynamic characteristics of the equipment (such as generator start-up delay, energy storage charging and discharging rate, etc.), to ensure that the simulation results can truly reflect the actual operation state of the power distribution network after the execution of the instructions, and provide a reliable basis for verifying the effectiveness of the control instructions.

[0083] The time scale of the deduction process simulation covers the complete period of instruction execution, from the issuance of the first instruction to the completion of all instruction execution and the system reaching a new steady state or quasi-steady state. During the simulation execution, the engine strictly follows the physical laws and dynamic characteristic models of the equipment for calculation. For example, when simulating an instruction to "increase the active output of a photovoltaic inverter by 10%", the engine will calculate the impact of this operation on the local node voltage, and then deduce the cascading impact on adjacent nodes and feeders through power flow calculation; when simulating an instruction to "close the tie-in switch", the engine will calculate the network topology changes, power flow redistribution and possible circulating current caused by this; when simulating the instruction "start the diesel generator and connect it", the engine will consider the start-up delay, ramp rate and its adjustment effect on system frequency of the generator. The deduction process will record the change trajectory of all key parameters in the simulation system, including but not limited to node voltage, line current and power, system frequency, load rate of key equipment (such as transformers), and whether the protection device acts, etc.

[0084] According to the deduction result, the control instruction verification unit performs effectiveness verification. The verification standard is a series of safety and performance constraints defined in advance and can be configured. The core constraints include: whether the voltage of all nodes remains within the allowed upper and lower limit range after simulation execution; whether the load of all lines and devices exceeds their safe capacity; whether the system frequency is stable within a small range near the rated value; whether the initial flexibility drop problem that triggered this control decision is eliminated (for example, whether the power shortage is made up and the voltage limit is corrected); whether new and more serious stability problems (such as voltage collapse, device overload, protection misoperation, etc.) are triggered. The control instruction verification unit compares and analyzes the data recorded in the deduction with these constraint conditions one by one.

[0085] When the deduction result meets all the preset safety and performance constraints, the control instruction verification unit determines that the operation control instruction set is verified. The verified instruction set is marked as valid and allowed to be issued to the field execution mechanism (such as a power generation controller, a circuit breaker controller, and a energy storage management system) through a communication interface for actual execution. When the deduction result fails to meet one or more constraint conditions, the control instruction verification unit determines that the verification fails. At this time, the unit generates a detailed instruction adjustment signal. The signal not only contains the verification failure flag, but also contains specific diagnostic information indicating which constraint conditions are not met and the key problem points observed in the deduction process (for example, “the voltage of node N12 drops to 0.85pu below the lower limit”, “the load rate of feeder L7 reaches 120%”, “the frequency drops to 49.2Hz at the lowest”). The diagnostic information is also associated with the specific instruction or instruction combination that caused the problem.

[0086] The instruction adjustment signal is fed back to the elastic operation control decision module in real time. After receiving the adjustment signal, the elastic operation control decision module starts the iterative optimization process of the operation control instruction set. The iterative process can take various strategies: modifying the instruction parameters that triggered the problem (such as reducing the power upregulation from 10% to 5%); adjusting the execution order or time point of the instruction (such as delaying the start time of a certain generator to avoid superimposed impact); replacing part of the instruction (such as replacing the start of energy storage discharge with increasing the output of a certain photovoltaic power station); or even completely abandoning the current strategy, selecting or generating an alternative control instruction set in the strategy library. The newly generated or modified instruction set will be sent to the control instruction verification unit again for a new round of deduction verification. The iterative cycle of “generation-verification-adjustment” will continue until a set of instructions that can pass all verification constraints is generated, or the maximum number of iterations is reached (at this time, a higher level of alarm or manual intervention may be triggered).

[0087] The system architecture also includes a permission management module that manages the access and operation permissions of different user roles to each function module of the entire system. The permission levels are clearly divided into three levels: read-only level, operation level, and management level. Users at the read-only level can only view the status information, monitoring data, and evaluation results of each module of the system, and have no right to make any configuration modifications or issue instructions. Users at the operation level have the permission to perform routine operations, such as confirming alarm information, manually triggering state evaluation, viewing and confirming (but not modifying) the automatically generated set of operation control instructions by the system, and executing the issuance after authorization. Users at the management level have the highest permission to modify system configuration parameters, manage user accounts and permissions, and manually generate and issue control instructions in certain special cases.

[0088] The permission management is implemented through a strict user identity authentication mechanism, such as a combination of a username and password with a digital certificate. After the user logs in, their operation request will be checked by an access control list that defines in detail the operation permissions (read, write, execute) of each role to each module, each function menu, and even each data item. All key operations of users, especially configuration modifications and instruction issuance, will be recorded in detail in the operation log, including the operator, operation time, operation content, and operation result, meeting the audit requirements.

[0089] The system architecture also includes a system operation monitoring module that continuously monitors the operation status of the three core modules: distributed power source real-time state monitoring module, power distribution network resilience state evaluation module, and resilience operation control decision module. It captures and analyzes the data flow between modules, monitors key indicators such as whether the data acquisition and transmission rate is normal, whether there is abnormal delay in data processing, whether the communication between modules is smooth, whether the internal computing tasks are completed on time, and whether error or alarm logs are generated. The module usually provides a comprehensive visual monitoring interface in the form of charts or dashboards to visually display the real-time health status, performance indicators, and statistical information of key data flows of each module. When any module is found to have an abnormal operation status, the module will generate corresponding alarm information to notify the operation and maintenance personnel to intervene and handle in a timely manner, ensuring the stable and reliable operation of the entire control system.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some or all of the technical features; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A distributed power supply based power distribution network elastic operation control system, characterized in that: The application relates to a power distribution network real-time state monitoring and control method and device. The application comprises: A distributed power source real-time state monitoring module is used for collecting the running state data of all the distributed power sources connected to a power distribution network and performing standardization processing on the running state data to generate a distributed power source running state data set; A power distribution network elasticity state evaluation module is used for calculating and grading the overall elasticity state of the power distribution network according to the distributed power source running state data set, generating the elasticity state grade of the power distribution network and an associated influence parameter set; 2.The power distribution network resilience control system based on distributed power supply of claim 1, wherein: An elasticity operation control decision module is used for generating a running control instruction set for different distributed power sources based on the elasticity state grade of the power distribution network and the associated influence parameter set, and dynamically controlling the power distribution network according to the running control instruction set. The distributed power source real-time state monitoring module identifies the abnormal state type in the running state data through the obtained running state data of the distributed power sources; The identified abnormal state type is subjected to state feature coding to obtain a state feature identification code, and the state feature identification code is compared with a known sample state type recorded in a state feature identification code mapping table; When there is no sample state type matching the abnormal state type in the state feature identification code mapping table, the state feature identification code is initialized as a specific numerical value; 3. The power distribution network resilience control system based on distributed power supply according to claim 2, characterized in that: When there is a sample state type matching the abnormal state type in the state feature identification code mapping table, the state feature identification code corresponding to the matching sample state type is associated to the abnormal state type. The distributed power source real-time state monitoring module locates the region to which the associated distributed power source belongs according to the state feature identification code of the non-specific numerical value, and updates the abnormal state count corresponding to the region; The region identifier, topological position information, state feature identification code and abnormal occurrence time of the abnormal state distributed power source are extracted, sorted and combined to generate a region running state sequence; 4. The power distribution network resilience control system based on distributed power supply according to claim 3, characterized in that: According to the region identifier in the region running state sequence, the region running state atlas in the power distribution network elasticity state evaluation module is updated, the region running state atlas is a graphical data model for power distribution network elasticity analysis, contains the physical connection topology of the power distribution network, and is superimposed with a real-time running state layer, the atlas nodes are divided into two categories: the bottom layer nodes map the actual electrical nodes, which represent the real-time collected data; the upper layer nodes represent the logical regions, which represent the data aggregated by the bottom layer nodes in the jurisdiction area, the node attributes include the number of normal and abnormal distributed power sources currently connected to the node, the load level and the voltage state, the edges in the atlas are used to represent the connection relationship; wherein, the edges representing the lines have the load rate as the attribute, and the edges representing the switches have the on-off state as the attribute. The power distribution network elasticity state evaluation module triggers an evaluation instruction when the region running state atlas is updated; The updated region is marked as a target evaluation region; The total number of the distributed power sources connected to the target evaluation region and the number of the distributed power sources in abnormal states are counted; The elasticity state identifier corresponding to the target evaluation region is output through data integration according to the region elasticity evaluation calculation rule.

5. The power distribution network resilience control system based on distributed power supply according to claim 4, characterized in that: The power distribution network resilience state evaluation module comprises a resilience state level mapping module, which determines the resilience state level corresponding to the target evaluation area according to the value of the resilience state identifier; When the resilience state identifier is a specific value, it is determined that the resilience state of the target evaluation area is normal; When the resilience state identifier is a non-specific value, a deep evaluation instruction is triggered and secondary data integration is performed through resilience impact degree calculation rules, and the resilience impact level identifier corresponding to the target evaluation area is output, which includes a first level and a second level.

6. The power distribution network resilience control system based on distributed power supply according to claim 5, characterized in that: The resilience state level mapping module marks the target evaluation area corresponding to the resilience impact level identifier with a value of the first level as a low resilience impact area; The target evaluation area corresponding to the resilience impact level identifier with a value of the second level is marked as a high resilience impact area; The resilience operation control decision module generates corresponding operation control instruction set according to the marking of the low resilience impact area or the high resilience impact area.

7. The power distribution network resilience control system based on distributed power supply according to claim 2, characterized in that: The power distribution network resilience state evaluation module obtains all state characteristic identifier codes with non-specific values; The occurrence frequency and the value sum of different state characteristic identifier codes are counted; The type impact value corresponding to different abnormal state types is calculated through the state type impact analysis rule. 8.The power distribution network resilience control system based on distributed power supply of claim 7, wherein: The resilience operation control decision module maintains the original operation control strategy for the abnormal state type with a specific state value, and generates an operation control strategy optimization instruction for the abnormal state type with a non-specific state value, wherein the specific state value is a type impact threshold value preset in the system. 9.The power distribution network resilience control system based on distributed power supply of claim 7, wherein: The resilience operation control decision module comprises a control instruction verification unit, which performs power distribution network operation state deduction according to the operation control instruction set, and verifies the effectiveness of the operation control instruction set according to the deduction result; When the verification fails, an instruction adjustment signal is generated and fed back to the resilience operation control decision module for operation control instruction set iteration.

10. The distributed power supply based power distribution network resilience operation control system according to any one of claims 1-9, characterized in that: The system further comprises: The permission management module is configured to configure the operation permission level of different users for the distributed power source real-time state monitoring module, the power distribution network resilience state evaluation module and the resilience operation control decision module; The system operation monitoring module is configured to monitor the running state data flow of the distributed power source real-time state monitoring module, the power distribution network resilience state evaluation module and the resilience operation control decision module.

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