Power distribution network region collaborative optimization control method, system, equipment and medium

Abstract

The invention discloses a power distribution network regional collaborative optimization control method, system and device and a medium, and relates to the technical field of power distribution network distributed collaborative optimization, and the method comprises the steps: determining an initial partition of a power distribution network based on multi-dimensional target data fusion, and obtaining the initial partition; determining a target area needing to be optimized according to the real-time operation parameters of the initial partitions based on the optimization key indexes; obtaining operation data of the target area and the neighborhood thereof, judging whether the neighborhood satisfies an optimization adjustment strategy, and regulating and controlling the optimization operation mode of the target area and the neighborhood thereof based on a judgment result; according to the method, the partitioning precision is improved through dynamic partitioning, the whole-region synchronous iteration is not needed, the linkage neighborhood operation state is combined, the optimization efficiency and the inter-domain collaboration are improved, the collaborative optimization reliability is further improved, and the collaborative optimization efficiency is improved. The problem that the optimization effect is poor due to the fact that a power distribution network multi-target inter-domain collaborative optimization control technology is insufficient in partition precision and cannot give consideration to distributed collaborative control efficiency is solved.

Description

Technical Field

[0001] This application relates to the field of distributed collaborative optimization technology for power distribution networks, specifically to a regional collaborative optimization control method, system, equipment, and medium for power distribution networks. Background Technology

[0002] The continuous development of smart grid technology has driven the evolution of distribution network optimization control towards multi-objective, distributed, and collaborative approaches. In complex distribution networks connected to multiple microgrids, achieving inter-domain collaborative optimization control has become a core issue for improving power quality, reducing operating costs, and enhancing system reliability. However, existing multi-objective inter-domain collaborative optimization control methods for distribution networks still have certain limitations in terms of dynamic partitioning accuracy, distributed solution algorithm efficiency, and the coordination between upper and lower level models, affecting their performance in practical applications.

[0003] The patent "A Multi-Objective Domain-Based Cooperative Distributed Optimization Control Method for Distribution Networks" (Publication No. CN114977274B) discloses a method for dynamically partitioning the distribution network based on node voltage sensitivity and establishing an upper-level optimization model. The upper-level optimization model is solved using an improved penalty factor modulation fully distributed ADMM algorithm with active power loss and voltage quality in each region as objective functions, yielding the common tie-line power between the distribution network and the microgrid. Subsequently, a lower-level optimization model is constructed, with microgrid operating cost as the objective function, and solved. However, this scheme relies solely on node voltage sensitivity for dynamic partitioning, neglecting the impact of load fluctuations and distributed generation output uncertainties on the partitioning results. This may lead to insufficient partitioning accuracy. Furthermore, the improved penalty factor modulation fully distributed ADMM algorithm may have slow convergence speed when dealing with large-scale distribution networks, affecting real-time performance. The coordination between the upper and lower-level models is weak, failing to fully integrate the power balance constraints within the microgrid with the overall optimization objectives of the distribution network, thus affecting the globality and reliability of the optimization results. Summary of the Invention

[0004] The purpose of this application is to address the problem that existing multi-objective domain collaborative optimization control technologies for distribution networks suffer from poor optimization results due to insufficient partitioning accuracy and inability to simultaneously ensure distributed collaborative control efficiency. This application proposes a regional collaborative optimization control method, system, equipment, and medium for distribution networks. By fusing multi-dimensional target data to dynamically partition the distribution network, the method improves partitioning accuracy and ensures strong correlation between nodes within each partition. Furthermore, it judges the optimization status based on the operating status and key indicators of each partition and performs optimization adjustments and scheduling, thereby improving collaborative control efficiency and further enhancing collaborative optimization efficiency and reliability.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a method for coordinated optimization control of a distribution network area. The method includes: determining an initial partition of the distribution network based on multi-dimensional target data fusion, and obtaining the initial partition; determining the target area to be optimized based on optimization key indicators and the real-time operating parameters of each initial partition; obtaining the operating data of the target area and its neighboring areas, determining whether the neighboring areas meet the optimization adjustment strategy, and adjusting the optimized operating mode of the target area and its neighboring areas based on the determination result.

[0006] This solution uses multi-dimensional target data fusion to dynamically partition the distribution network, making the partitions more closely match the actual operation scenarios of the distribution network and avoiding inefficient collaborative optimization caused by unreasonable partitioning, thus improving partitioning accuracy. By optimizing key indicators, the solution identifies the target areas that need to be optimized, thereby adjusting the collaborative optimization of the target areas and their related areas without requiring synchronous iteration across the entire region, indirectly improving the efficiency of distributed optimization. By linking the operating status of neighboring areas, the solution adjusts the optimization mode of neighboring areas to avoid resource contention and power imbalance between the target area and its neighbors during the optimization process, thereby strengthening inter-domain collaboration. The dynamic collaboration between the target area and its neighbors avoids local optimization from affecting global stability, improves inter-domain collaboration, and further enhances the reliability of collaborative optimization.

[0007] Preferably, the step of determining the initial partition of the distribution network based on multi-dimensional target data fusion and obtaining the initial partition includes: taking minimizing the voltage deviation within the partition and maximizing the complementary output of distributed power sources as dynamic partitioning objectives, fusing power signal data of distribution network nodes, historical load data, distributed power source installed capacity distribution data and microgrid access location data to divide the distribution network into several independent control domains, and obtaining the initial partition containing the regional boundary, the adjacent domain list and the microgrid domain identifier.

[0008] Preferably, the step of determining the target region to be optimized based on the real-time operating parameters of each initial partition, according to the optimization key indicators, includes: determining the key indicators for the region to enter optimization and the region to exit optimization; calculating the corresponding adaptive indicator thresholds based on the historical best range of the key indicators and the real-time operating conditions, wherein each key indicator includes a first adaptive indicator threshold corresponding to the entry judgment condition and a second adaptive indicator threshold corresponding to the exit judgment condition; obtaining the real-time operating parameters of each initial partition, comparing them with the corresponding first adaptive indicator thresholds, and determining whether the initial partition meets the conditions for entering optimization; if any key indicator of the initial partition meets the conditions for entering optimization, then it is determined as the target region.

[0009] Preferably, the key indicators include at least load fluctuation indicators, voltage quality indicators, active power loss indicators, tie-line power deviation indicators between the initial partition and neighboring areas, communication delay of the initial partition, and optimization adjustment time indicators.

[0010] Preferably, the step of determining the target area to be optimized based on the real-time operating parameters of each initial partition according to the optimization key indicators further includes: judging the operating scenario of the target area based on the voltage quality index change trend of the target area, so as to determine the optimization adjustment time required for the target area.

[0011] Preferably, the step of acquiring the operational data of the target region and its neighbors, determining whether the neighbors meet the optimization adjustment strategy, and adjusting the optimization operation mode of the target region and its neighbors based on the determination result includes: comparing the real-time operational parameters of the target region with the second adaptive threshold to determine whether the target region meets the conditions for exiting optimization; if any key indicator of the target region meets the conditions for exiting optimization, then controlling the target region to exit the optimization operation mode; before the target region starts optimization, acquiring the real-time operational parameters of the target region's neighbors based on the neighboring domain list, determining the optimization adjustment status of the target region's neighbors, and adjusting the optimization operation mode of the target region's neighbors based on the optimization adjustment status.

[0012] Preferably, adjusting the optimized operation mode of the target area's neighborhood based on the optimized adjustment state includes: if the target area's neighborhood is already in an optimized state, shortening its optimization adjustment time to allow the neighborhood to exit optimization before the target area's optimization ends; if the remaining optimization adjustment time of the target area's neighborhood is greater than or equal to the target area's optimization adjustment time, controlling the neighborhood to exit optimization; if the target area's neighborhood is a control domain carrying a microgrid identifier and in optimized operation mode, determining its remaining optimization adjustment time and power loss status; if the control domain's power loss exceeds a power loss threshold and the remaining optimization time is greater than a power loss safety time threshold, controlling the control domain to exit optimization and replenish power loss; if the control domain reaches the optimization adjustment time and its power loss is lower than the power loss threshold, controlling the control domain to exit optimization.

[0013] Secondly, embodiments of this application provide a distribution network area collaborative optimization control system, including: a dynamic partitioning module, used to determine the initial partitioning of the distribution network based on multi-dimensional target data fusion, and obtain the initial partitioning; a target optimization module, used to determine the target area to be optimized based on optimization key indicators and the real-time operating parameters of each initial partition; and a collaborative optimization module, used to obtain the operating data of the target area and its neighboring areas, determine whether the neighboring areas meet the optimization adjustment strategy, and adjust the optimization operation mode of the target area and its neighboring areas based on the determination result.

[0014] Thirdly, embodiments of this application provide a computer device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to implement the steps of the method described in the first aspect when executing the program stored in the memory.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0016] The beneficial effects of this application are: 1. By dynamically fusing multi-dimensional target data to determine the initial partitioning of the distribution network, the problem of insufficient partitioning accuracy and disconnection from real-time operating conditions caused by solid-state operating data or reliance on the voltage sensitivity of a single node is overcome. The fusion of multiple targets and multiple data ensures that the partitioning results not only guarantee the operational stability within the domain, but also clarify the boundaries, adjacency relationships and microgrid attributes of each region, providing a clear control object for subsequent target area selection and inter-domain collaborative optimization. 2. By using key indicators and adaptive threshold mechanisms, the target areas that truly need optimization can be accurately identified to avoid ineffective and missed optimizations, reduce resource waste and strategy conflicts, and thus improve optimization control efficiency. Among them, the adaptive threshold ensures that the optimization triggering or exit criteria are adapted to dynamic operating conditions. 3. By adjusting the neighborhood optimization mode through linkage with the neighborhood operation status, problems such as resource contention and power imbalance between the target area and the neighborhood during the optimization process are avoided. This ensures that the optimization is terminated in a timely manner after the desired effect is achieved, avoids over-regulation, strengthens inter-domain collaboration, and ultimately achieves global optimization. Attached Figure Description

[0017] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0018] Figure 1 A flowchart of a regional collaborative optimization control method for a power distribution network is provided in this application embodiment.

[0019] Figure 2 This is a schematic diagram of a regional collaborative optimization control system module for a power distribution network, provided as an embodiment of this application.

[0020] Figure 3 This is a schematic diagram of a method for collaborative optimization of a target region and its neighborhood, provided in an embodiment of this application.

[0021] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Example 1: As Figure 1 As shown, a regional collaborative optimization control method for a distribution network includes steps S1-S3, wherein: S1. Determine the initial partition of the distribution network based on the fusion of multi-dimensional target data and obtain the initial partition.

[0024] As an optional implementation, step S1 includes: With the goal of minimizing voltage deviation within a partition and maximizing the complementary output of distributed power sources, the distribution network is divided into several independent control domains by integrating power signal data of distribution network nodes, historical load data, distributed power source installed capacity distribution data, and microgrid access location data, and an initial partition containing regional boundaries, a list of adjacent domains, and microgrid domain identifiers is obtained.

[0025] In some embodiments, the power signal data includes at least voltage sensitivity data. The k-means algorithm is used to minimize the voltage deviation within the partition and maximize the complementary output of distributed power sources as the dynamic partitioning objectives. The distribution network is divided into several independent control domains, and the adjacency relationship of each domain is defined. Domains containing microgrids are identified as microgrid domains, providing judgment criteria and control boundaries for subsequent optimization.

[0026] In this embodiment, by fusing multi-objective and multi-dimensional data, the partitioning results ensure both intra-domain operational stability (i.e., minimum voltage deviation) and improved energy utilization efficiency (i.e., maximum complementary output of distributed power sources). Dynamic partitioning clarifies the boundaries, adjacency relationships, and microgrid attributes of each region, providing clear control objects and associated logic for subsequent target region selection and inter-domain collaborative optimization. Simultaneously, it avoids inter-domain interference caused by unreasonable partitioning. It is understandable that a clear list of adjacent domains and microgrid domain identifiers eliminates the need for additional redundant calculations in subsequent inter-domain data interaction and strategy collaboration, thereby improving the overall optimization response speed.

[0027] S2. Based on the optimization of key indicators, determine the target area to be optimized according to the real-time operating parameters of each initial partition.

[0028] As an optional implementation, step S2 includes: Identify key indicators for regional entry optimization and regional exit optimization; Based on the historical optimal range of the key indicators and the real-time operating conditions, the corresponding adaptive indicator thresholds are calculated. Each key indicator includes a first adaptive indicator threshold corresponding to the entry into the optimization judgment condition and a second adaptive indicator threshold corresponding to the exit from the optimization judgment condition. The real-time operating parameters of each initial partition are obtained and compared with the corresponding first adaptive index threshold to determine whether the initial partition meets the conditions for entering the optimization of the region. If any key index of the initial partition meets the conditions for entering the optimization of the region, it is determined as the target region.

[0029] As an optional implementation, the key indicators include at least load fluctuation indicators, voltage quality indicators, active power loss indicators, tie-line power deviation indicators between the initial partition and neighboring areas, communication delay of the initial partition, and optimization adjustment time indicators.

[0030] As an optional implementation, the step of determining the target area to be optimized based on key optimization indicators and the real-time operating parameters of each initial partition further includes: Based on the voltage quality index change trend of the target area, the operating scenario of the target area is determined, so as to determine the optimization and adjustment time required for the target area.

[0031] Specifically, the historical operating data of each region (i.e., the initial partition) includes at least load fluctuations and voltage acceptable ranges; based on the key indicators for entering and exiting optimization, the real-time operating conditions are used as dynamic correction items, and adaptive thresholds are calculated with the benchmark values ​​(i.e., historical optimal ranges) of each key indicator, thereby obtaining an adaptive threshold table for each region, where each adaptive threshold corresponds to different judgment conditions.

[0032] In some examples, the first load fluctuation threshold process includes: acquiring historical load data for the initial partition to calculate the base fluctuation amplitude; acquiring real-time weather information, distributed power source type distribution, and user electricity consumption behavior characteristics for the initial partition to calculate the correction amplitude; and adding the base fluctuation amplitude and the correction amplitude to obtain the first load fluctuation amplitude threshold.

[0033] Furthermore, the first adaptive index threshold includes at least the first load fluctuation amplitude threshold, the first voltage quality index threshold, the first active power loss threshold, the first neighborhood connection power deviation threshold, the first communication delay threshold, and the first data transmission error threshold; when any key index of a certain region meets the conditions compared with the corresponding first adaptive threshold, it indicates that the region needs to enter optimization.

[0034] Furthermore, the conditions for determining whether a region needs to enter optimization mode include: load fluctuation amplitude greater than or equal to a first load fluctuation amplitude threshold, voltage quality less than a first voltage quality index threshold, active power loss greater than or equal to a first active power loss threshold, microgrid internal power balance constraints not being triggered, tie-line power deviation between the region and its neighbors less than a first neighbor tie-line power deviation threshold, and the region's communication delay within a first communication delay threshold and data transmission error less than a first data transmission error threshold. When at least one of the above conditions is met, the region is determined to need to enter optimization adjustment mode.

[0035] In this embodiment, key indicators and an adaptive threshold mechanism are used to achieve precise selection of optimization targets and reasonable allocation of optimization resources. Specifically: the comparison conditions of the adaptive threshold can adapt to different regions and different operating conditions, improving the accuracy of optimization triggering. Optimization is only locked in the target region and related regions, avoiding indiscriminate iteration across the entire region, thereby improving the optimization rate; optimization time planning based on voltage change trends avoids over- or under-optimization, which is conducive to improving resource utilization.

[0036] S3. Obtain the operational data of the target area and its neighborhood, determine whether the neighborhood meets the optimization adjustment strategy, and adjust the optimization operation mode of the target area and its neighborhood based on the judgment result.

[0037] As an optional implementation, step S3 includes: Based on the real-time operating parameters of the target region, they are compared with the second adaptive threshold to determine whether the target region meets the conditions for exiting optimization. If any key indicator of the target region meets the conditions for exiting optimization, the target region is controlled to exit the optimization operation mode. Before optimization is initiated in the target area, the real-time operating parameters of the target area's neighbors are obtained based on the neighboring domain list, the optimization adjustment status of the target area's neighbors is determined, and the optimization operation mode of the target area's neighbors is adjusted based on the optimization adjustment status.

[0038] In some embodiments, determining whether a target area meets the conditions for exiting optimization includes: the optimization adjustment time reaches an optimization time threshold, the voltage quality index of the target area exceeds a second voltage quality index threshold, the power balance constraint within the microgrid is triggered, the power deviation of the tie lines between the target area and adjacent areas is greater than or equal to a second neighboring area tie power deviation threshold, and the communication delay of the target area is greater than a second communication delay threshold or the data transmission error is greater than a first data transmission error threshold. When at least one of the above conditions is met, it is determined that the target area can exit the optimization adjustment mode.

[0039] In some implementations, the target area uses a hierarchical communication architecture to collect operational data from adjacent domains, including at least load, distributed generation output, remaining optimization time, and microgrid power balance status. If communication in a domain is interrupted, temporary operational parameters are generated using historical data and data predictions to ensure uninterrupted interaction. This allows the optimization strategy to simultaneously address multiple issues such as voltage deviation, excessive network losses, and tie-line overload, thereby meeting the multi-objective collaborative requirements of modern smart grids.

[0040] As an optional implementation, adjusting the optimized operation mode of the target region's neighborhood based on the optimized adjustment state includes: If the neighborhood of the target region is already in an optimized state, then shorten its optimization adjustment time so that the neighborhood exits optimization before the optimization of the target region ends. If the remaining optimization adjustment time of the neighborhood of the target region is greater than or equal to the optimization adjustment time of the target region, then the neighborhood is controlled to exit optimization; If the neighboring domain of the target area is a control domain carrying a microgrid identifier and in optimized operation mode, then determine its remaining optimization adjustment time and power loss status. If the power loss of the control domain exceeds the power loss threshold and the remaining optimization time is greater than the power loss safety time threshold, then the control domain is controlled to exit optimization and power loss is replenished; if the control domain reaches the optimization adjustment time and the power loss is lower than the power loss threshold, then the control domain is controlled to exit optimization.

[0041] In this embodiment, the dynamic determination mechanism for the target region to exit optimization ensures that the optimization is terminated in a timely manner after the desired effect is achieved, avoiding over-regulation; by adjusting the neighboring region's optimization mode in conjunction with the neighboring region's operating status, problems such as resource contention and power imbalance between the target region and its neighbors during the optimization process are avoided, thus strengthening inter-domain collaboration.

[0042] Example 2, as Figure 2 As shown, a regional collaborative optimization control system for a power distribution network includes: The dynamic partitioning module is used to determine the initial partitioning of the distribution network based on the fusion of multi-dimensional target data and to obtain the initial partitioning. The target optimization module is used to determine the target area to be optimized based on the key optimization indicators and the real-time operating parameters of each initial partition. The collaborative optimization module is used to acquire the operational data of the target area and its neighbors, determine whether the neighbors meet the optimization adjustment strategy, and adjust the optimization operation mode of the target area and its neighbors based on the judgment result.

[0043] As an optional implementation, the dynamic partitioning module is specifically used to: divide the distribution network into several independent control domains by integrating power signal data of distribution network nodes, historical load data, distributed power generation installed capacity distribution data, and microgrid access location data, with the goal of minimizing voltage deviation within the partition and maximizing the complementary output of distributed power sources, and obtain an initial partition that includes regional boundaries, a list of adjacent domains, and microgrid domain identifiers.

[0044] As an optional implementation, the target optimization module specifically includes: a first target optimization unit, used to determine key indicators for region entry into optimization and region exit from optimization, wherein: the key indicators include at least load fluctuation indicators, voltage quality indicators, active power loss indicators, tie-line power deviation indicators between the initial partition and neighboring areas, communication delay of the initial partition, and optimization adjustment time indicators; a second target optimization unit, used to calculate corresponding adaptive indicator thresholds based on the historical optimal range of the key indicators and real-time operating conditions, each key indicator including a first adaptive indicator threshold corresponding to the entry into optimization judgment condition and a second adaptive indicator threshold corresponding to the exit from optimization judgment condition; a third target optimization unit, used to obtain the real-time operating parameters of each initial partition, compare them with the corresponding first adaptive indicator thresholds, and determine whether the initial partition meets the conditions for region entry into optimization. If any key indicator of the initial partition meets the conditions for region entry into optimization, then it is determined as the target region.

[0045] As an optional implementation, the target optimization module further includes: a fourth target optimization unit, used to determine the operating scenario of the target area based on the voltage quality index change trend of the target area, so as to determine the optimization adjustment time required for the target area.

[0046] As an optional implementation, the collaborative optimization module specifically includes: a first collaborative optimization unit, used to compare the real-time operating parameters of the target region with the second adaptive threshold to determine whether the target region meets the conditions for exiting optimization; if any key indicator of the target region meets the conditions for exiting optimization, then the target region is controlled to exit the optimization operation mode; and a second collaborative optimization unit, used to obtain the real-time operating parameters of the target region's neighbors based on the neighboring domain list before the target region starts optimization, determine the optimization adjustment status of the target region's neighbors, and adjust the optimization operation mode of the target region's neighbors based on the optimization adjustment status.

[0047] As an optional implementation, the second collaborative optimization unit specifically includes: if the neighborhood of the target area is already in an optimized state, shortening its optimization adjustment time to adjust the neighborhood to exit optimization before the target area finishes optimization; if the remaining optimization adjustment time of the neighborhood of the target area is greater than or equal to the optimization adjustment time of the target area, controlling the neighborhood to exit optimization; if the neighborhood of the target area is a control domain carrying a microgrid identifier and in an optimized operation mode, determining its remaining optimization adjustment time and power loss status; if the power loss of the control domain exceeds the power loss threshold and the remaining optimization time is greater than the power loss safety time threshold, controlling the control domain to exit optimization and replenish power loss; if the control domain reaches the optimization adjustment time and the power loss is lower than the power loss threshold, controlling the control domain to exit optimization.

[0048] In some examples, such as Figure 3 As shown, the operational status information of target region A is obtained by comparing key indicators with the corresponding first adaptive threshold to determine whether it meets the requirements for entering the optimization and adjustment mode. If it does, target region A is controlled to enter the optimization and adjustment mode. Then, the operational status information of other regions (i.e., the neighborhood of target region A) is obtained to determine whether the neighborhood meets the conditions for exiting the optimization mode or adjusting its optimization time. If it does, the optimization mode is exited or its optimization time is adjusted. If it does not meet the conditions, the optimization operation status of the target region is continuously monitored until it completes optimization and meets the exit conditions.

[0049] In this embodiment, the initial partitioning of the distribution network is determined by dynamic fusion of multi-dimensional target data. This overcomes the problems of insufficient partitioning accuracy and disconnection from real-time operating conditions caused by solid-state operating data or reliance on the voltage sensitivity of a single node. The fusion of multiple targets and multiple data ensures that the partitioning results not only guarantee the operational stability within the domain but also clarify the boundaries, adjacency relationships, and microgrid attributes of each region, providing clear control objects for subsequent target region selection and inter-domain collaborative optimization. The key indicators and adaptive threshold mechanism can accurately identify the target regions that truly need optimization, thereby avoiding ineffective optimization and missed optimization, reducing resource waste and strategy conflicts, and thus improving optimization control efficiency. Among them, the adaptive threshold ensures that the optimization triggering or exit criteria are adapted to dynamic operating conditions. By adjusting the neighboring region's optimization mode through linkage with the neighboring region's operating status, problems such as resource contention and power imbalance between the target region and its neighbors during the optimization process are avoided, ensuring that the optimization is terminated in a timely manner after the desired effect is achieved. This avoids over-regulation and strengthens inter-domain collaboration, ultimately achieving global optimization.

[0050] This application also provides a computer device, such as... Figure 4 As shown, it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to execute programs stored in memory to implement a regional collaborative optimization control method for power distribution networks.

[0051] The communication bus mentioned in the above computer equipment can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0052] The communication interface is used for communication between the aforementioned computer equipment and other devices.

[0053] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0054] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0055] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a regional collaborative optimization control method for a power distribution network.

[0056] The above-described embodiments are preferred embodiments of this application and are not intended to limit the specific scope of this application. The scope of this application includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape, structure, and method of this application are within the protection scope of this application.

Claims

1. A power distribution network regional collaborative optimization control method, characterized in that: The method comprises the following steps: Determine the initial partition of the power distribution network based on multi-dimensional target data fusion, and obtain the initial partition; Determine the target area to be optimized according to the real-time operation parameters of each initial partition based on the optimization key indicators; Obtain the operation data of the target area and its neighborhood, judge whether the neighborhood meets the optimization adjustment strategy, and regulate the optimization operation mode of the target area and its neighborhood based on the judgment result.

2. The power distribution network regional collaborative optimization control method according to claim 1, characterized in that: The method for determining the initial partition of the power distribution network based on multi-dimensional target data fusion and obtaining the initial partition comprises: The power distribution network is divided into several independent control domains by fusing the power signal data, historical load data, distributed power installation distribution data and micro-grid access location data of the power distribution network nodes, and the initial partition containing the area boundary, adjacent area list and micro-grid domain identifier is obtained, with the minimization of voltage deviation in the partition and the maximization of complementary output of the distributed power as the dynamic partition target.

3. The power distribution network regional collaborative optimization control method of claim 1, wherein: The method for determining the target area to be optimized according to the real-time operation parameters of each initial partition based on the optimization key indicators comprises: Determine the key indicators for the entry and exit of the area into optimization; Calculate the corresponding adaptive indicator threshold value according to the historical optimal range of the key indicators combined with the real-time working condition, each key indicator comprises a first adaptive indicator threshold value corresponding to the entry optimization judgment condition and a second adaptive indicator threshold value corresponding to the exit optimization judgment condition; Obtain the real-time operation parameters of each initial partition, compare them with the corresponding first adaptive indicator threshold value respectively, judge whether the initial partition meets the condition for the entry of the area into optimization, and if any one of the key indicators of the initial partition meets the condition for the entry of the area into optimization, the initial partition is determined as the target area.

4. The power distribution network regional collaborative optimization control method of claim 3, wherein: The key indicators at least include the load fluctuation indicator, the voltage quality indicator, the active power loss indicator, the tie-line power deviation indicator between the initial partition and the neighborhood, the communication delay and the optimization adjustment time indicator of the initial partition.

5. The power distribution network regional collaborative optimization control method according to claim 4, characterized in that: The method for determining the target area to be optimized according to the real-time operation parameters of each initial partition based on the optimization key indicators further comprises: Judge the operation scene where the target area is located according to the voltage quality indicator change trend of the target area, to determine the optimization adjustment time required by the target area.

6. The power distribution network regional coordinated optimization control method according to claim 2 or 3, characterized in that: The method for obtaining the operation data of the target area and its neighborhood, judging whether the neighborhood meets the optimization adjustment strategy, and regulating the optimization operation mode of the target area and its neighborhood based on the judgment result comprises: Compare the real-time operation parameters of the target area with the second adaptive threshold value, judge whether the target area meets the condition for the exit of the area from optimization, and if any one of the key indicators of the target area meets the condition for the exit of the area from optimization, control the target area to exit the optimization operation mode; Before the target area starts optimization, obtain the real-time operation parameters of the target area neighborhood based on the adjacent area list, judge the optimization adjustment state of the target area neighborhood, and adjust the optimization operation mode of the target area neighborhood based on the optimization adjustment state.

7. The power distribution network regional collaborative optimization control method of claim 6, wherein: The method for adjusting the optimization operation mode of the target area neighborhood based on the optimization adjustment state comprises: If the neighborhood of the target region is in an optimized state, shorten the optimization adjustment time thereof to adjust the neighborhood to exit optimization before the optimization of the target region ends; If the remaining optimization adjustment time of the neighborhood of the target region is greater than or equal to the optimization adjustment time of the target region, control the neighborhood to exit optimization; If the neighborhood of the target region is a control domain carrying a micro-grid identifier and in an optimized operation mode, determine the remaining optimization adjustment time and the power loss thereof; If the power loss of the control domain exceeds a power loss threshold and the remaining optimization time is greater than a power loss safety time threshold, control the control domain to exit optimization and compensate for the power loss; if the control domain reaches the optimization adjustment time and the power loss is lower than the power loss threshold, control the control domain to exit optimization.

8. A power distribution network regional collaborative optimization control method system, suitable for a power distribution network regional collaborative optimization control method according to any one of claims 1-7, characterized in that: The method comprises the following steps: a dynamic partition module is configured to determine an initial partition of the power distribution network based on multi-dimensional target data fusion and obtain the initial partition; a target optimization module is configured to determine a target region to be optimized based on an optimization key indicator and real-time operation parameters of each initial partition; a cooperative optimization module is configured to obtain operation data of the target region and its neighborhood, determine whether the neighborhood meets an optimization adjustment strategy, and control the optimization operation mode of the target region and its neighborhood based on the determination result.

9. A computer device, comprising: The method comprises the following steps: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are in communication with each other through the communication bus; the memory is configured to store a computer program; and the processor is configured to execute the program stored in the memory to implement the steps of the power distribution network region cooperative optimization control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer program is stored in the computer readable storage medium and is executed by the processor to implement the steps of the power distribution network region cooperative optimization control method according to any one of claims 1-7.

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