A distribution network load and voltage quality collaborative optimization system and method

Abstract

The application discloses a kind of distribution network load and voltage quality collaborative optimization system and method, system includes multi-source data acquisition module, index coupling analysis module, multi-objective cross-regulation module and core computing module replacement unit, the access and standardization cleaning of distribution network full-dimensional operation data are completed by multi-source data acquisition module, the coupling relationship model of distribution network load and voltage quality is constructed by index coupling analysis module and distributed dynamic weight of working condition adaptation, equivalent replacement and reconstruction of core computing logic are completed in the premise of keeping external business process and interface unchanged of existing distribution network analysis system by core computing module replacement unit, finally, distribution network collaborative control instruction is generated and issued by multi-objective cross-regulation module.The application does not need to reconstruct existing system business process, and has low upgrade cost and strong adaptability, can significantly improve distribution network load balancing rate, voltage qualification rate and operation and maintenance efficiency, and is suitable for power supply service command system distribution network operation and maintenance quality and efficiency control all scenarios.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation control and digital optimization technology, and in particular to a system and method for coordinated optimization of power distribution network load and voltage quality. Background Technology

[0002] The distribution network is the core link connecting the power generation side and the user side of the power system. Its operational stability, load balance, and voltage quality directly determine the reliability of power supply and the user's electricity experience. In recent years, the State Grid Corporation of China has continuously promoted the special action to improve the efficiency and quality of distribution network operation and maintenance, and has clearly required the improvement of the level of lean management and control of the distribution network through digital means. As the core carrier of distribution network operation and maintenance management and control, the power supply service command system has realized basic functions such as distribution network operation data collection, work order management and control, and basic statistical analysis, and is widely used in four-level operation and maintenance scenarios at the provincial, municipal, county, and team levels.

[0003] Currently, in the operation of distribution networks, problems such as line overload, three-phase imbalance of distribution transformers, and voltage exceeding limits are intertwined. Optimizing a single indicator often leads to the deterioration of other indicators, becoming a core pain point in distribution network operation and maintenance management. In existing technologies, distribution network load optimization and voltage quality management mostly adopt an independent and separate approach: on the one hand, load regulation often focuses on balancing line load rates as a single objective, without considering the impact of load transfer on regional voltage quality, easily leading to secondary problems such as low voltage and voltage exceeding the upper limit; on the other hand, voltage quality optimization relies heavily on conventional methods such as reactive power compensation and tap adjustment, without combining load distribution optimization to address voltage anomalies at their root, resulting in poor sustainability of the management effect. At the same time, the core calculation modules of existing distribution network analysis systems mostly use fixed threshold judgment modes, which have poor adaptability to different regions and operating conditions, and insufficient optimization accuracy; if the system is upgraded and transformed, it often requires the reconstruction of the entire business process and external interfaces, resulting in high transformation costs, long cycles, and difficulty in adapting to the information operation level of grassroots operation and maintenance personnel in municipal and county companies. In addition, existing optimization schemes mostly rely on offline statistical data, which lacks real-time performance and cannot adapt to the dynamic fluctuation characteristics of distribution network load. This makes it difficult to meet the needs of real-time control of distribution network operation and maintenance, thus hindering the improvement of the practicality of the power supply service command system. Summary of the Invention

[0004] This invention addresses the pain points of existing distribution network load and voltage quality optimization being independent, which easily leads to secondary operational problems, as well as the high cost of upgrading existing systems and weak adaptability to operating conditions. It provides a system and method for collaborative optimization of distribution network load and voltage quality, which can seamlessly reuse the existing distribution network management system architecture, realize collaborative optimization of load and voltage, and improve the reliability and operation and maintenance efficiency of distribution network.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows: A distribution network load and voltage quality collaborative optimization system includes a multi-source data acquisition module, an index coupling analysis module, a multi-objective cross-control module, and a core calculation module replacement unit. The multi-source data acquisition module acquires raw operational data from all dimensions of the distribution network and performs standardized cleaning to obtain standardized distribution network operational data, which is then sent to the index coupling analysis module. The index coupling analysis module extracts indices, models coupling relationships, and calculates dynamic weights from the standardized distribution network operational data to obtain a distribution network load-voltage quality coupling relationship model and dynamic weight results for each index, which is then sent to the multi-objective cross-control module. The core calculation module replacement unit, while retaining the existing external business processes and interfaces of the distribution network analysis system, performs equivalent replacement and logical reconstruction of the traditional core calculation module for distribution network load and voltage quality analysis to obtain an optimized core calculation module, which is then applied to the multi-objective cross-control module. The multi-objective cross-control module receives the coupling relationship model, the dynamic weight results of each index, and the computational support of the optimized core calculation module, performs multi-objective cross-coordinated control processing on the distribution network operation status, obtains distribution network coordinated control instructions, and sends them to the distribution network field execution equipment.

[0006] Furthermore, the multi-source data acquisition module includes an interface adaptation submodule, a data cleaning submodule, and a data storage submodule. The interface adaptation submodule receives raw, multi-dimensional distribution network operation data from the distribution automation system, electricity consumption information acquisition system, power grid resource business platform, and marketing business system, and sends it to the data cleaning submodule. The raw, multi-dimensional distribution network operation data includes distribution line load rate, transformer three-phase imbalance, real-time node voltage value, voltage qualification rate, line power supply range, and basic equipment ledger information. The data cleaning submodule performs outlier removal, duplicate data deduplication, and format standardization on the raw, multi-dimensional distribution network operation data to obtain standardized distribution network operation data, which is then sent to the data storage submodule and the indicator coupling analysis module. The data storage submodule performs distributed storage processing on the standardized distribution network operation data to obtain a persistently stored distribution network operation dataset and updates it in real time.

[0007] Furthermore, the index coupling analysis module includes an index extraction submodule, a coupling relationship modeling submodule, and a dynamic weight calculation submodule. The index extraction submodule receives the standardized distribution network operation data, performs load-related and voltage quality-related index extraction processing, obtains load-related index sets and voltage quality index sets, and sends them to the coupling relationship modeling submodule. The load-related index sets include line overload rate, distribution transformer overload rate, and load fluctuation amplitude. The voltage quality index sets include voltage upper limit rate, voltage lower limit rate, voltage qualification rate, and three-phase voltage imbalance. The coupling relationship modeling submodule uses a grey relational analysis algorithm to calculate the correlation degree of the load-related index sets and voltage quality index sets, constructs a distribution network load-voltage quality coupling relationship model, and sends it to the dynamic weight calculation submodule and the multi-objective cross-control module. The dynamic weight calculation submodule receives the distribution network load-voltage quality coupling relationship model and real-time distribution network operation data, uses the analytic hierarchy process (AHP) to dynamically assign weights to each index, obtains the dynamic weight results of each index under different operating conditions, and sends them to the multi-objective cross-control module.

[0008] Furthermore, the core computing module replacement unit includes an original module interface adaptation submodule, a new computing module deployment submodule, and a logic reconstruction submodule. The original module interface adaptation submodule receives the external business interface protocol and data interaction format information of the existing power distribution network analysis system, performs interface adaptation and compatibility processing on it, obtains an interface adaptation scheme that seamlessly connects with the existing power supply service command system, and sends it to the new computing module deployment submodule. The new computing module deployment submodule receives the interface adaptation scheme, replaces the traditional threshold judgment calculation module with a multi-objective optimization calculation module based on the improved particle swarm optimization algorithm, completes the deployment processing of the new computing module, obtains a basic version optimized core computing module, and sends it to the logic reconstruction submodule. The logic reconstruction submodule uses the maximization of load balancing rate and voltage qualification rate as optimization objectives, and the rated operating parameters of equipment and line power supply capacity as constraints, to reconstruct the computational logic of the basic version optimized core computing module, obtains the optimized core computing module, and empowers it to the multi-objective cross-control module, providing computational support for the generation of control strategies.

[0009] Furthermore, the multi-objective cross-control module includes a control strategy generation submodule, a power supply range adjustment submodule, and a control command issuance submodule. The control strategy generation submodule receives the distribution network load-voltage quality coupling relationship model, the dynamic weight results of each indicator, and the computational support of the optimized core calculation module. It performs multi-objective collaborative control strategy generation processing on the distribution network operation status to obtain a basic control strategy set including line load transfer, distribution transformer tap adjustment, and reactive power compensation device switching, and sends it to the power supply range adjustment submodule. The power supply range adjustment submodule receives the basic control strategy set, optimizes and adjusts the power supply range of adjacent distribution lines, obtains a multi-objective cross-control strategy that takes into account both load balance and voltage quality improvement, and sends it to the control command issuance submodule. The control command issuance submodule standardizes and converts the multi-objective cross-control strategy into an instruction, obtains distribution network collaborative control instructions, and sends them to the distribution network field execution equipment.

[0010] Furthermore, the system also includes an effect monitoring and dynamic optimization module; the effect monitoring and dynamic optimization module includes a real-time monitoring submodule, an effect evaluation submodule, and a parameter optimization submodule; the real-time monitoring submodule receives the distribution network coordinated control command and the distribution network operation feedback data after control transmitted from the distribution automation system, performs real-time acquisition and status monitoring processing on the distribution network operation feedback data after control, obtains the distribution network control real-time operation index set, and sends it to the effect evaluation submodule; the effect evaluation submodule uses load balancing improvement rate, voltage qualification rate improvement rate, and control command execution response time as evaluation indicators to quantitatively evaluate the control effect of the distribution network control real-time operation index set, obtains the distribution network coordinated control effect quantitative evaluation result, and sends it to the parameter optimization submodule; the parameter optimization submodule performs reverse optimization adjustment on the coupling relationship model correlation coefficient, control strategy execution parameters, and particle swarm algorithm iteration parameters according to the control effect quantitative evaluation result, obtains the optimized model parameters, control strategy parameters, and calculation module operation parameters, and transmits them back to the index coupling analysis module, multi-objective cross-control module, and core calculation module replacement unit, respectively, to realize the update and optimization of the parameters of each module.

[0011] Furthermore, the system also includes a visualization module, which receives the standardized distribution network operation data, the distribution network load-voltage quality coupling relationship model, the distribution network coordinated control instructions and the quantitative evaluation results of the control effect, performs visualization conversion processing on the data into charts and topology diagrams, obtains the visualization display results of the distribution network load-voltage quality coordinated optimization, and provides hierarchical viewing and operation interaction services to the provincial, municipal and county-level operation and maintenance personnel.

[0012] Furthermore, the visualization module integrates an early warning submodule; the early warning submodule receives the real-time operation index set after the distribution network regulation and compares it with the preset distribution network safe operation threshold, the preset distribution network safe operation threshold corresponds to the rated operation parameter standard of distribution network equipment and lines; when the load index or voltage quality index in the real-time operation index set after the distribution network regulation exceeds the preset distribution network safe operation threshold, an abnormal distribution network operation early warning information is generated and pushed to the mobile terminal of the corresponding operation and maintenance personnel and the main station of the power supply service command system.

[0013] Furthermore, the system also includes a data interaction bus; the data interaction bus is communicatively connected to the multi-source data acquisition module, the indicator coupling analysis module, the multi-objective cross-control module, the core calculation module replacement unit, and the effect monitoring and dynamic optimization module, respectively, to realize data interaction and command transmission between the modules.

[0014] A method for co-optimizing distribution network load and voltage quality, applied to the aforementioned distribution network load and voltage quality co-optimization system, includes the following steps: S1. The multi-source data acquisition module receives raw operation data of the distribution network in all dimensions, performs standardized cleaning processing on the raw operation data of the distribution network in all dimensions, obtains standardized distribution network operation data, and sends it to the index coupling analysis module. S2. The index coupling analysis module sequentially performs index extraction, coupling relationship modeling and dynamic weight calculation on the standardized distribution network operation data to obtain the distribution network load-voltage quality coupling relationship model and the dynamic weight results of each index, and sends them to the multi-objective cross-control module. S3. The core computing module replacement unit replaces the traditional core computing module for distribution network load and voltage quality analysis with equivalent replacement and reconstructs the operation logic while keeping the external business processes and interfaces of the existing distribution network analysis system unchanged. The optimized core computing module is then empowered to the multi-objective cross-control module to provide computational support for the generation of control strategies. S4. Based on the coupling relationship model, the dynamic weight results of each indicator, and the computational support of the optimized core calculation module, the multi-objective cross-coordinated control module performs multi-objective cross-coordinated control processing on the distribution network operation status, obtains the distribution network coordinated control instruction, and sends it to the distribution network field execution equipment.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a load-voltage coupling relationship model and adopts a multi-objective cross-regulation strategy to simultaneously improve regional voltage quality while optimizing load balance. This fundamentally avoids secondary problems such as voltage exceeding limits and load imbalance caused by optimizing a single indicator. This solution achieves dual optimization of distribution network load balance and voltage quality, stably controlling distribution network operation indicators within the acceptable range required by the State Grid distribution network operation regulations, significantly improving the stability and reliability of distribution network operation. Through an equivalent replacement design of the core computing module, the external business interfaces and data interaction formats of the existing power supply service command system are fully retained, only the underlying core computing logic is replaced. There is no need to reconstruct the overall business process or interrupt the operation of the existing system. Compared with traditional system transformation solutions, this significantly reduces upgrade and transformation costs, greatly shortens the project implementation cycle, and can be quickly adapted to the existing State Grid power supply service command system architecture without requiring large-scale replacement of hardware and software equipment at the grassroots level. This invention employs an improved particle swarm optimization algorithm with dynamic weights, which significantly improves the convergence speed of the optimization algorithm and enables dynamic optimization and adjustment of the distribution network operation status. At the same time, it uses the analytic hierarchy process to achieve dynamic weight allocation under different operating conditions, enabling differentiated optimization for different scenarios such as heavily loaded lines, low-voltage areas, and routine operation. Compared with the traditional fixed threshold judgment mode, the adaptability of operating conditions and optimization accuracy are significantly improved, and it can perfectly adapt to distribution network operation scenarios with different regions and different load characteristics.

[0016] This invention constructs a fully automated system encompassing data acquisition, automatic analysis, strategy generation, command issuance, and effect feedback. It eliminates the need for frontline maintenance personnel to perform complex multi-dimensional data statistical analysis and parameter settings; they only need to execute standardized control commands. This significantly reduces the information technology requirements for city and county company maintenance personnel, effectively minimizes errors caused by manual statistics, and significantly improves the efficiency of related work, perfectly meeting the actual needs of frontline maintenance scenarios. In terms of economic benefits, load balancing optimization and improved voltage quality effectively reduce distribution network line losses, minimize insulation aging losses caused by equipment overload, extend equipment lifespan, and achieve effective control over distribution network maintenance costs. In terms of social benefits, improving power supply reliability and voltage quality significantly reduces user complaints related to low voltage and power outages, improving customer electricity experience and fully complying with the core requirements of the State Grid's special action to improve the quality and efficiency of distribution network operation and maintenance. Attached Figure Description

[0017] Figure 1 This is the overall architecture diagram of the distribution network load and voltage quality collaborative optimization system of the present invention; Figure 2 This is a flowchart illustrating the implementation of the method for coordinated optimization of distribution network load and voltage quality according to the present invention. Detailed Implementation

[0018] To facilitate understanding of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the invention and should not be considered as specific limitations thereof.

[0019] In the following description of the embodiments, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of a described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0022] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. References to "one embodiment" or "some embodiments" in this application mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized. System Implementation Example:

[0023] This embodiment uses the distribution network as the application scenario and is built upon the existing power supply service command system. The system has been integrated with the Marketing 2.0 business system, the electricity consumption information collection system, the distribution automation system, and the power grid resource business platform. Figure 1 As shown, the specific implementation is as follows: Multi-source data acquisition module: The multi-source data acquisition module is used to acquire raw operation data of the distribution network in all dimensions and perform standardized cleaning processing to obtain standardized distribution network operation data and send it to the indicator coupling analysis module.

[0024] The multi-source data acquisition module includes an interface adaptation submodule, a data cleaning submodule, and a data storage submodule. The interface adaptation submodule supports protocols such as HTTP, MQTT, and IEC104, and accesses real-time data on 10kV lines and three-phase voltage, current, active power, and reactive power from the distribution automation system; daily frozen data and voltage limit exceedance data from the electricity consumption information acquisition system; user profiles and transformer area information from the marketing business system; and equipment ledger data from the power grid resource business platform. The data sampling period is set to 5 minutes, and the data format strictly follows the State Grid CIM unified model standard. The data cleaning submodule uses the 3σ criterion to remove outliers, eliminating invalid data exceeding ±3 standard deviations of the equipment's rated parameters, deduplicating duplicate data based on equipment ID and timestamp, and standardizing the format according to the State Grid CIM model. The data storage submodule uses the distributed time-series database Influx DB to achieve persistent storage and rapid retrieval of distribution network operation data.

[0025] Indicator Coupling Analysis Module: The indicator coupling analysis module extracts indicators, models coupling relationships, and calculates dynamic weights from standardized distribution network operation data to obtain the distribution network load-voltage quality coupling relationship model and the dynamic weight results of each indicator, and sends them to the multi-objective cross-control module.

[0026] The indicator coupling analysis module includes an indicator extraction submodule, a coupling relationship modeling submodule, and a dynamic weight calculation submodule. The module extracts load-related indicators (10kV line overload rate, distribution transformer heavy load rate, load fluctuation amplitude, three-phase imbalance) and voltage quality indicators (voltage exceeding upper limit rate, voltage exceeding lower limit rate, voltage qualification rate, voltage deviation value) from standardized data. The coupling relationship modeling module uses a grey relational analysis algorithm to calculate the correlation between the two types of indicators, setting the resolution coefficient ρ=0.5, and using 96 points / day of continuous 7-day load data. Indicators with a correlation ≥0.6 are selected as strongly coupled indicators, thus constructing a load-voltage coupling relationship model. The dynamic weight calculation module uses the analytic hierarchy process (AHP) to construct a judgment matrix, strictly controlling the consistency ratio CR of the judgment matrix to <0.1 (meeting consistency verification requirements). Dynamic weight allocation is completed according to the real-time operating conditions of the distribution network: under heavy load conditions, the weight of load-related indicators is 0.7, and the weight of voltage quality indicators is 0.3; under low voltage conditions, the weight of load-related indicators is 0.3, and the weight of voltage quality indicators is 0.7; under normal operating conditions, the weight of both types of indicators is 0.5.

[0027] Core Computing Module Replacement Unit: While keeping the external business processes and interfaces of the existing distribution network analysis system unchanged, the core computing module replacement unit performs equivalent replacement and reconstructs the operation logic of the traditional core computing module for distribution network load and voltage quality analysis, resulting in an optimized core computing module, which is then empowered to the multi-objective cross-control module.

[0028] The core computing module replacement unit includes an original module interface adaptation submodule, a new computing module deployment submodule, and a logic reconstruction submodule. The original module interface adaptation submodule fully retains the existing power supply service command system's WebService interface and data interaction format, seamlessly integrating with the existing system's work order module and statistical analysis module without modifying existing business processes. The new computing module deployment submodule uses an improved particle swarm optimization algorithm to replace the original traditional fixed threshold judgment module. The logic reconstruction submodule uses maximizing load balancing rate and voltage qualification rate as dual optimization objectives, constrained by the aforementioned distribution network operation procedures, to complete the core computational logic reconstruction. The core parameters of the improved particle swarm optimization algorithm are set as follows: population size N=50, maximum number of iterations T_max=200, inertia weight w adopts a linear decreasing strategy (initial w_start=0.9, iteration end w_end=0.4), individual learning factor c1=2.0, social learning factor c2=2.0, velocity boundary v_max=±0.5, and position boundary matches the aforementioned distribution network equipment rated parameter constraints. The optimized module can be directly embedded into the existing power supply service command system for stable operation.

[0029] Multi-objective cross-control module: The multi-objective cross-control module receives the coupling relationship model, the dynamic weight results of each indicator, and the operation support of the optimized core calculation module. It performs multi-objective cross-coordinated control processing on the distribution network operation status, obtains the distribution network coordinated control instructions, and sends them to the distribution network field execution equipment.

[0030] The multi-objective cross-control module includes a control strategy generation submodule, a power supply range adjustment submodule, and a control command issuance submodule. The control strategy generation submodule, based on a coupled model, dynamic weights, and optimized calculation module, generates a basic control strategy that includes line load transfer, transformer tap adjustment, and reactive power compensation device switching. The power supply range adjustment submodule uses a neighborhood search algorithm to determine the optimal power supply range division point between adjacent lines, setting the neighborhood search radius to three adjacent transformer areas, an upper limit of 50 iterations, and a convergence threshold of load rate deviation ≤2%, achieving load transfer while ensuring the area voltage remains within the acceptable range. The control command issuance submodule converts the control strategy into standardized IEC104 protocol commands and issues them to the FTU and TTU execution devices in the distribution network, while simultaneously pushing them to the power supply service command system main station and the mobile terminals of maintenance personnel.

[0031] The Effect Monitoring and Dynamic Optimization Module includes a real-time monitoring submodule, an effect evaluation submodule, and a parameter optimization submodule. The real-time monitoring submodule collects distribution network operation data after regulation at 5-minute intervals and extracts a set of real-time operation indicators. The effect evaluation submodule uses load balancing improvement rate, voltage qualification rate improvement rate, regulation command response time, and line loss reduction rate as core evaluation indicators to quantitatively evaluate the regulation effect. The parameter optimization submodule performs reverse optimization for indicators that do not meet the standards: when the load balancing improvement rate is less than 10%, the particle swarm size is increased to 80 and the maximum number of iterations is increased to 300; when the voltage qualification rate improvement is less than 0.2%, the inertia weight is adjusted from 0.95 to linearly decreasing to 0.3, and the learning factors are adjusted to c1=2.2 and c2=1.8. The optimized parameters are then fed back to the corresponding modules to complete the closed-loop update.

[0032] Visualization Module: The visualization module is built using a B / S architecture, supporting three-level hierarchical access control from the provincial to the municipal to the county level. It receives standardized distribution network operation data, distribution network load-voltage quality coupling relationship models, distribution network collaborative control instructions, and quantitative evaluation results of control effects. The displayed content includes a distribution network load distribution heat map, voltage quality topology map, control strategy list, and optimization effect comparison curves. The integrated early warning sub-module uses the rated parameters of the State Grid distribution network operation regulations as safety thresholds. A yellow warning is triggered when the load rate is ≥80% and the voltage deviation exceeds ±7%, and a red warning is triggered when the load rate is ≥100% and the voltage deviation exceeds ±10%. The warning information is pushed to the corresponding operation and maintenance personnel in real time.

[0033] The data interaction bus is connected to the multi-source data acquisition module, the indicator coupling analysis module, the multi-objective cross-control module, the core calculation module replacement unit, the visualization display module, and the effect monitoring and dynamic optimization module to realize data interaction and command transmission between the modules. Method Implementation Examples:

[0034] This embodiment is based on the above system and takes a 10kV line area as the implementation scenario. This area includes 3 10kV main lines and 28 distribution transformers. There are problems such as overload on the No. 1 main line, low voltage in 6 distribution areas, and excessive three-phase imbalance of the distribution transformers. The specific implementation steps are as follows: S1. Multi-source data acquisition and standardized cleaning: The multi-source data acquisition module accesses the real-time operation data of the distribution automation system in this area, the daily frozen data of the distribution transformer in the electricity information acquisition system, and the equipment ledger data of the power grid resource business platform, with a sampling period of 5 minutes; abnormal data is removed by using the 3σ criterion, and duplicate data is deduplicated and standardized in CIM format to obtain standardized distribution network operation data, which is then sent to the indicator coupling analysis module.

[0035] S2. Index Coupling Analysis and Dynamic Weight Calculation: The index coupling analysis module extracts load-related and voltage quality-related indicators. Using the grey relational analysis algorithm (ρ=0.5), the grey relational degree between line load rate and transformer area voltage qualification rate is calculated to be 0.72, which belongs to strongly coupled indicators. A load-voltage coupling relationship model is constructed. There are heavily loaded lines in this area. The analytic hierarchy process is used to set the weight of load-related indicators to 0.7 and the weight of voltage quality-related indicators to 0.3. The consistency ratio of the judgment matrix CR=0.07<0.1, which meets the consistency verification requirements. The coupling model and dynamic weight results are sent to the multi-objective cross-control module.

[0036] S3. Equivalent Replacement and Logical Reconstruction of Core Computing Module: The core computing module replacement unit retains the existing external interfaces and business processes of the power supply service command system, deploys an improved multi-objective optimization computing module using the particle swarm optimization algorithm, and replaces the original fixed threshold judgment module. With the optimization objectives of maximizing load balancing rate and maximizing voltage qualification rate, and with the constraints of line load rate ≤70% and voltage deviation ±7%, the particle swarm optimization algorithm parameters are set as follows: population size N=50, maximum number of iterations T_max=200, inertia weight linearly decreasing from 0.9 to 0.4, and learning factor c1=c2=2.0. The optimized core computing module is then used to power the multi-objective cross-control module.

[0037] S4. Multi-objective cross-control and command generation and issuance: Based on the input data and the optimized calculation module, the multi-objective cross-control module generates basic control strategies: adjust the interconnection switch between main line 1 and main line 2 to transfer the heavy load of main line 1; adjust the tap position of 3 distribution transformers to +2.5% and switch the reactive power compensation capacitor banks of 4 transformer areas; use the neighborhood search algorithm (neighborhood radius of 3 transformer areas) to optimize the power supply range division point to ensure that the voltage of the area after load transfer is within the qualified range; convert the control strategy into standardized commands and issue them to the field FTU / TTU equipment, and simultaneously push them to the power supply service command system and the mobile terminal of maintenance personnel.

[0038] S5. Effect Monitoring and Closed-Loop Parameter Optimization: The effect monitoring and dynamic optimization module collects operational data after regulation in real time. The quantitative evaluation results are as follows: the load rate of the No. 1 main line decreased from 85% to 62%, the line load balance rate increased by 27%, the voltage qualification rate of the distribution area increased from 98.2% to 99.85%, and the three-phase imbalance of the distribution transformer decreased from 18% to 9%. All indicators have reached the preset targets. The optimized algorithm parameters and weight parameters are sent back to the preceding module to complete the closed-loop optimization. The visualization module displays the optimization effect simultaneously. No indicators exceeded the limit and no warning was triggered.

[0039] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0040] In various embodiments, the hardware implementation of the technology can directly utilize existing smart devices, including but not limited to industrial control computers, PCs, smartphones, handheld devices, and floor-standing devices. Its input device preferably uses an on-screen keyboard, its data storage and computing modules utilize existing memory, calculators, and controllers, its internal communication modules utilize existing communication ports and protocols, and its remote communication utilizes existing GPRS networks, the World Wide Web, etc.

[0041] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0042] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0043] In the various embodiments of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units. If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

Claims

1. A distribution network load and voltage quality collaborative optimization system, characterized in that, The system includes a multi-source data acquisition module, an indicator coupling analysis module, a multi-objective cross-control module, and a core calculation module replacement unit. The multi-source data acquisition module acquires raw operational data from all dimensions of the distribution network and performs standardized cleaning to obtain standardized distribution network operational data, which is then sent to the indicator coupling analysis module. The indicator coupling analysis module extracts indicators, models coupling relationships, and calculates dynamic weights from the standardized distribution network operational data to obtain a distribution network load-voltage quality coupling relationship model and dynamic weight results for each indicator, which is then sent to the multi-objective cross-control module. The core calculation module replacement unit, while retaining the existing external business processes and interfaces of the distribution network analysis system, performs equivalent replacement and logical reconstruction of the traditional core calculation module for distribution network load and voltage quality analysis, resulting in an optimized core calculation module, which is then applied to the multi-objective cross-control module. The multi-objective cross-control module receives the coupling relationship model, the dynamic weight results of each indicator, and the computational support of the optimized core calculation module. It performs multi-objective cross-coordinated control processing on the distribution network operation status, obtains the distribution network coordinated control instruction, and sends it to the distribution network field execution equipment.

2. The distribution network load and voltage quality collaborative optimization system according to claim 1, characterized in that, The multi-source data acquisition module includes an interface adaptation submodule, a data cleaning submodule, and a data storage submodule. The interface adaptation submodule receives raw, multi-dimensional distribution network operation data from the distribution automation system, electricity consumption information acquisition system, power grid resource business platform, and marketing business system, and sends it to the data cleaning submodule. The raw, multi-dimensional distribution network operation data includes distribution line load rate, transformer three-phase imbalance, real-time node voltage value, voltage qualification rate, line power supply range, and basic equipment ledger information. The data cleaning submodule performs outlier removal, duplicate data deduplication, and format standardization on the raw, multi-dimensional distribution network operation data to obtain standardized distribution network operation data, which is then sent to the data storage submodule and the indicator coupling analysis module. The data storage submodule performs distributed storage processing on the standardized distribution network operation data to obtain a persistently stored distribution network operation dataset and updates it in real time.

3. The distribution network load and voltage quality collaborative optimization system according to claim 1, characterized in that, The index coupling analysis module includes an index extraction submodule, a coupling relationship modeling submodule, and a dynamic weight calculation submodule. The index extraction submodule receives the standardized distribution network operation data, performs load-related and voltage quality-related index extraction processing, obtains a load-related index set and a voltage quality index set, and sends them to the coupling relationship modeling submodule. The load-related index set includes line overload rate, distribution transformer overload rate, and load fluctuation amplitude. The voltage quality index set includes voltage upper limit rate, voltage lower limit rate, voltage qualification rate, and three-phase voltage imbalance. The coupling relationship modeling submodule uses a grey relational analysis algorithm to calculate the correlation degree of the load-related index set and the voltage quality index set, constructs a distribution network load-voltage quality coupling relationship model, and sends it to the dynamic weight calculation submodule and the multi-objective cross-control module. The dynamic weight calculation submodule receives the distribution network load-voltage quality coupling relationship model and the real-time operating condition data of the distribution network, uses the analytic hierarchy process to dynamically assign weights to each indicator, obtains the dynamic weight results of each indicator under different operating conditions, and sends them to the multi-objective cross-control module.

4. The distribution network load and voltage quality collaborative optimization system according to claim 1, characterized in that, The core computing module replacement unit includes an original module interface adaptation submodule, a new computing module deployment submodule, and a logic reconstruction submodule. The original module interface adaptation submodule receives external business interface protocols and data interaction format information from the existing power distribution network analysis system, performs interface adaptation and compatibility processing on them, obtains an interface adaptation scheme that seamlessly integrates with the existing power supply service command system, and sends it to the new computing module deployment submodule. The new computing module deployment submodule receives the interface adaptation scheme, replaces the traditional threshold judgment calculation module with a multi-objective optimization calculation module based on an improved particle swarm optimization algorithm, completes the deployment processing of the new computing module, obtains a basic version optimized core computing module, and sends it to the logic reconstruction submodule. The logic reconstruction submodule, with the optimization objectives of maximizing load balancing rate and voltage qualification rate, and with the constraints of equipment rated operating parameters and line power supply capacity, performs computational logic reconstruction on the basic version optimized core computing module, obtaining an optimized core computing module and empowering it to the multi-objective cross-control module, providing computational support for the generation of control strategies.

5. The distribution network load and voltage quality collaborative optimization system and method according to claim 1, characterized in that, The multi-objective cross-control module includes a control strategy generation submodule, a power supply range adjustment submodule, and a control command issuance submodule. The control strategy generation submodule receives the distribution network load-voltage quality coupling relationship model, the dynamic weight results of each indicator, and the computational support from the optimized core calculation module. It performs multi-objective collaborative control strategy generation processing on the distribution network operation status, obtaining a basic control strategy set including line load transfer, distribution transformer tap adjustment, and reactive power compensation device switching, and sends it to the power supply range adjustment submodule. The power supply range adjustment submodule receives the basic control strategy set, optimizes and adjusts the power supply range of adjacent distribution lines, obtaining a multi-objective cross-control strategy that balances load balancing and voltage quality improvement, and sends it to the control command issuance submodule. The control command issuance submodule standardizes and converts the multi-objective cross-control strategy into commands, obtaining distribution network collaborative control commands, and sends them to the distribution network field execution equipment.

6. The distribution network load and voltage quality collaborative optimization system according to claim 1, characterized in that, The system also includes an effect monitoring and dynamic optimization module; the effect monitoring and dynamic optimization module includes a real-time monitoring submodule, an effect evaluation submodule, and a parameter optimization submodule; The real-time monitoring submodule receives the distribution network coordinated control command and the distribution network operation feedback data after control transmitted from the distribution automation system. It performs real-time collection and status monitoring processing on the distribution network operation feedback data after control to obtain a set of real-time operation indicators after distribution network control and sends it to the effect evaluation submodule. The effect evaluation submodule uses load balancing improvement rate, voltage qualification rate improvement rate and control command execution response time as evaluation indicators to quantitatively evaluate the control effect of the real-time operation indicator set after the control of the distribution network, obtain the quantitative evaluation result of the distribution network coordinated control effect, and send it to the parameter optimization submodule. The parameter optimization submodule performs reverse optimization and adjustment on the correlation coefficient of the coupling relationship model, the execution parameters of the control strategy, and the iteration parameters of the particle swarm algorithm based on the quantitative evaluation results of the control effect, so as to obtain the optimized model parameters, control strategy parameters, and calculation module operation parameters, and respectively send them back to the index coupling analysis module, the multi-objective cross-control module, and the core calculation module replacement unit to realize the updating and optimization of the parameters of each module.

7. The distribution network load and voltage quality collaborative optimization system according to claim 6, characterized in that, The system also includes a visualization module, which receives the standardized distribution network operation data, the distribution network load-voltage quality coupling relationship model, the distribution network collaborative control instructions and the quantitative evaluation results of the control effect, performs visualization conversion processing on the data into charts and topology diagrams, obtains the visualization display results of the distribution network load-voltage quality collaborative optimization, and provides hierarchical viewing and operation interaction services to the provincial, municipal and county-level operation and maintenance personnel.

8. The distribution network load and voltage quality collaborative optimization system according to claim 7, characterized in that, The visualization module integrates an early warning sub-module; the early warning sub-module receives the real-time operation index set after the distribution network regulation and compares it with the preset distribution network safe operation threshold, which corresponds to the rated operation parameter standard of the distribution network equipment and lines. When the load index or voltage quality index in the real-time operation index set after the distribution network regulation exceeds the preset distribution network safe operation threshold, an abnormal distribution network operation warning message is generated and pushed to the mobile terminal of the corresponding operation and maintenance personnel and the main station of the power supply service command system.

9. The distribution network load and voltage quality collaborative optimization system according to claim 1, characterized in that, The system also includes a data interaction bus; the data interaction bus is communicatively connected to the multi-source data acquisition module, the indicator coupling analysis module, the multi-objective cross-control module, the core calculation module replacement unit, and the effect monitoring and dynamic optimization module, respectively, to realize data interaction and command transmission between the modules.

10. A method for co-optimizing distribution network load and voltage quality, applied to the distribution network load and voltage quality co-optimization system according to any one of claims 1-9, characterized in that, The method includes the following steps: S1. The multi-source data acquisition module receives raw operation data of the distribution network in all dimensions, performs standardized cleaning processing on the raw operation data of the distribution network in all dimensions, obtains standardized distribution network operation data, and sends it to the index coupling analysis module. S2. The index coupling analysis module sequentially performs index extraction, coupling relationship modeling and dynamic weight calculation on the standardized distribution network operation data to obtain the distribution network load-voltage quality coupling relationship model and the dynamic weight results of each index, and sends them to the multi-objective cross-control module. S3. The core computing module replacement unit replaces the traditional core computing module for distribution network load and voltage quality analysis with equivalent replacement and reconstructs the operation logic while keeping the external business processes and interfaces of the existing distribution network analysis system unchanged. The optimized core computing module is then empowered to the multi-objective cross-control module to provide computational support for the generation of control strategies. S4. Based on the coupling relationship model, the dynamic weight results of each indicator, and the computational support of the optimized core calculation module, the multi-objective cross-coordinated control module performs multi-objective cross-coordinated control processing on the distribution network operation status, obtains the distribution network coordinated control instruction, and sends it to the distribution network field execution equipment.

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