Voltage treatment method and device for power distribution network, medium and equipment

Abstract

The invention discloses a voltage treatment method and device for a power distribution network, a medium and equipment, and belongs to the field of voltage treatment, and the method comprises the steps: firstly obtaining an initial system parameter and an initial node voltage value of the power distribution network, and obtaining a reconstruction network, the parameter and a first-stage voltage value through a preset short-time scale power distribution network reconstruction model if the initial voltage is out of limit; if the first-stage voltage is still out of limit, obtaining a distributed resource optimization result and a second-stage voltage value by means of a preset short-time scale distributed resource optimization configuration model; if the second-stage voltage is still out of limit, obtaining a mobile energy storage scheduling path, a charging and discharging strategy and a third-stage voltage value by using a preset long-time scale mobile energy storage scheduling and operation model; and finally, the voltage of the power distribution network is governed according to the results, so that the problem that the voltage of the power distribution network cannot be governed accurately and efficiently in the prior art is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of voltage management, and more particularly to a method, apparatus, medium, and equipment for voltage management in power distribution networks. Background Technology

[0002] With continuous economic development and rising electricity demand year by year, the power distribution network, as a key link between the power system and users, faces higher requirements for its power supply level and quality. Traditional power distribution networks, mainly with a radial structure, rely on fixed topology and limited control methods, which are no longer sufficient to cope with the source-load structure changes brought about by the high proportion of new energy (such as photovoltaic and wind turbines) grid connection and the popularization of electric vehicles. The intermittency and volatility of new energy output and the randomness of load demand are superimposed, resulting in significant nonlinearity and uncertainty in the operation of the power distribution network. Problems such as increased voltage fluctuation amplitude and rising frequency of exceeding limits are becoming increasingly prominent, which not only affect the user's electricity experience but also threaten the safe and stable operation of the power distribution network. To alleviate the aforementioned problems, existing technologies have developed voltage management methods such as distribution network reconfiguration and distributed resource regulation. Distribution network reconfiguration changes the network topology by adjusting the states of sectionalizing switches and tie switches, which can reduce network losses and improve voltage distribution. However, this method only focuses on network structure optimization and does not fully integrate the flexible adjustment capabilities of distributed resources. Optimized configuration of distributed resources such as OLTCs (On-Load Tap Changers) and capacitor banks can suppress voltage fluctuations through active / reactive power regulation, but these methods are mostly short-term responses and cannot cope with long-term voltage exceedance scenarios. In addition, mobile energy storage, due to its spatiotemporal flexibility and adjustable charging / discharging status, is considered an important supplementary resource for distribution network voltage management. However, its scheduling needs to be combined with transportation network planning and long-term operational requirements. Existing technologies have not yet organically combined "short-term topology optimization and distributed resource regulation" with "long-term mobile energy storage scheduling," resulting in the inability of existing technologies to accurately and efficiently manage distribution network voltage. Summary of the Invention

[0003] This invention provides a method, apparatus, medium, and equipment for voltage management in power distribution networks, in order to solve the problem that existing technologies cannot accurately and efficiently manage the voltage of power distribution networks.

[0004] Firstly, this application provides a voltage management method for a distribution network, including: Obtain the initial system parameters and initial node voltage values ​​of the distribution network; If the initial node voltage value exceeds the limit, based on the initial system parameters and the initial node voltage value, combined with the preset short-timescale distribution network reconfiguration model, the reconfigured network, reconfigured network parameters, and first-stage node voltage values ​​are obtained; wherein, the short-timescale distribution network reconfiguration model is constructed and solved based on the objectives of minimum power loss, minimum number of switching operations, and minimum voltage deviation. If the voltage value of the first-stage node exceeds the limit, based on the reconstructed network, the reconstructed network parameters, and the voltage value of the first-stage node, combined with the preset short-timescale distributed resource optimization configuration model, the distributed resource optimization configuration result and the voltage value of the second-stage node are obtained; wherein, the short-timescale distributed resource optimization configuration model is based on the minimum power loss target and is constructed and solved by combining OLTC, capacitor bank, photovoltaic and wind turbine; If the voltage value of the second-stage node exceeds the limit, based on the distributed resource optimization configuration result and the voltage value of the second-stage node, combined with the preset long-term mobile energy storage scheduling and operation model, the long-term mobile energy storage scheduling path, the mobile energy storage charging and discharging state switching strategy, and the voltage value of the third-stage node are obtained; wherein, the long-term mobile energy storage scheduling and operation model is constructed and solved based on road network modeling and the minimum power loss target; The voltage of the distribution network is managed based on the mobile energy storage scheduling path, charging and discharging state switching strategy, and the third-stage node voltage value under the long-term dimension.

[0005] This application enables rapid identification of voltage exceedance situations by acquiring the initial system parameters and initial node voltage values ​​of the distribution network. When the initial node voltage value exceeds the limit, a short-timescale distribution network reconfiguration model based on minimum power loss, minimum number of switching operations, and minimum voltage deviation targets is used for network reconfiguration. This yields the reconfigured network and the first-stage node voltage values, which quickly adjusts the network structure and initially alleviates the voltage exceedance problem. If the first-stage node voltage value still exceeds the limit, a short-timescale distributed resource optimization configuration model is further combined to construct optimized configurations for OLTCs, capacitor banks, photovoltaics, and wind turbines, yielding the optimized configuration results and the second-stage node voltage values. This step further improves the voltage level by optimizing the configuration of distributed resources. If the second-stage node voltage value still exceeds the limit, a long-timescale mobile energy storage scheduling and operation model is finally combined to obtain the scheduling path, charging / discharging state switching strategy, and the third-stage node voltage values ​​for mobile energy storage. This step, through flexible scheduling and charging / discharging control of mobile energy storage, completely solves the voltage exceedance problem over a long time dimension. The entire process is progressive, comprehensively considering network structure, distributed resource allocation, and mobile energy storage scheduling. It not only effectively manages voltage exceedances but also reduces power loss, decreases the number of switching operations, and improves the overall operating efficiency and reliability of the distribution network. This application effectively solves the problem that existing technologies cannot accurately and efficiently manage voltage in distribution networks.

[0006] Furthermore, the initial system parameters of the distribution network include the resistance and reactance of each branch of the distribution network, the active power load and reactive power load of each node, the initial opening and closing status of the sectionalizing switch and tie switch, the initial adjustment level of the OLTC, the initial connected capacity of the capacitor bank, and the initial active and reactive power output of the photovoltaic and wind turbines.

[0007] This application provides accurate and comprehensive foundational data support for subsequent voltage mitigation by comprehensively acquiring initial system parameters of the distribution network. Specifically, the acquired parameters cover the resistance and reactance of each branch of the distribution network, the active and reactive power loads of each node, the initial opening and closing states of sectionalizing switches and tie switches, the initial adjustment level of the OLTC, the initial connected capacity of capacitor banks, and the initial active and reactive power outputs of photovoltaic and wind turbines. The comprehensive acquisition of these parameters enables the system to accurately understand the current operating status of the distribution network, thus providing accurate input data for subsequent voltage mitigation measures. Based on these detailed and comprehensive initial parameters, voltage mitigation models can be constructed and optimized more accurately, thereby achieving more effective voltage regulation and control, and improving the voltage stability and operating efficiency of the distribution network.

[0008] Furthermore, if the initial node voltage value exceeds the limit, based on the initial system parameters and the initial node voltage value, combined with a preset short-timescale distribution network reconfiguration model, the reconfigured network, reconfigured network parameters, and first-stage node voltage values ​​are obtained, specifically as follows: The initial node voltage value is compared with the preset acceptable range of node voltage. If the initial node voltage value exceeds the acceptable range of node voltage, it is confirmed that the initial node voltage value exceeds the limit. The initial system parameters and initial node voltage values ​​are input into the short-time-scale distribution network reconfiguration model. The nonlinear constraints in the short-time-scale distribution network reconfiguration model are subjected to second-order cone relaxation. The short-time-scale distribution network reconfiguration model after second-order cone relaxation is solved using the Gurobi solver to obtain the switching state optimization results. Based on the optimization results of the switch states, the opening and closing states of the sectionalizing switches and tie switches of the distribution network are adjusted to obtain the reconstructed network. Based on the topology information of the reconstructed network, the reconstructed network parameters are calculated; wherein, the reconstructed network parameters include the current, power and first-stage node voltage values ​​of each branch after reconstruction. The short-timescale distribution network reconfiguration model includes a comprehensive objective function and constraints. The comprehensive objective function is a weighted summation function of minimum power loss, minimum number of switching operations, and minimum voltage deviation. The constraints include power flow constraints, network security operation constraints, network radial constraints, and switching state constraints.

[0009] This application accurately locates voltage exceedance issues by comparing initial node voltage values ​​with acceptable ranges, providing targeted objectives for subsequent remediation and avoiding blind operations. Next, the initial system parameters are input into a short-timescale distribution network reconfiguration model. Combining second-order cone relaxation processing with the Gurobi solver eliminates the solution difficulties caused by model nonlinear constraints and quickly obtains optimized switching state results. This process relies on the model's comprehensive objective function of "minimum power loss, minimum number of switching operations, and minimum voltage deviation," which, while ensuring the radial structure and power flow safety of the power grid, also considers remediation efficiency and cost (reducing the number of switching operations). This involves reducing equipment losses and maintenance costs. Subsequently, the switch states are adjusted based on the optimization results to form a reconstructed network. Then, the reconstructed parameters are calculated based on the topology information. The resulting first-stage node voltage values ​​can improve voltage distribution and alleviate over-limit problems by optimizing the network topology. They can also reduce the power grid's energy consumption with minimal power loss, while avoiding equipment damage caused by excessive switch operation. This lays a better network foundation for the subsequent second-stage distributed resource optimization and configuration, achieving a progressive effect of "accurate diagnosis - efficient solution - low-consumption optimization - basic guarantee". This effectively solves the problems of difficult solution, single objective, and neglect of equipment losses in traditional distribution network reconstruction.

[0010] Furthermore, the comprehensive objective function is a weighted summation function of minimum power loss, minimum number of switching operations, and minimum voltage deviation, specifically: The sub-objectives of minimum power loss, minimum number of switching operations, and minimum voltage deviation are normalized to obtain the normalized sub-objectives. Assign weight coefficients to each normalized sub-objective; Based on the normalized sub-objectives and their corresponding weighting coefficients, the comprehensive objective function of the short-timescale distribution network reconfiguration model is obtained. The minimum power loss sub-target is obtained by calculating the sum of the products of the squares of the currents in all branches of the distribution network and the resistances in all branches. The minimum number of switching operations sub-target is obtained by counting the number of switches that change relative to the initial switching state. The minimum voltage deviation sub-target is obtained by calculating the sum of the absolute values ​​of the differences between the voltage values ​​of each node and the reference voltage.

[0011] The comprehensive objective function of the short-timescale distribution network reconfiguration model in this application achieves a more comprehensive and beneficial effect than single-objective optimization through a design logic of "multi-objective normalization-weight allocation-weighted summation". The specific reasoning is as follows: First, the three sub-objectives of minimum power loss, minimum number of switching operations, and minimum voltage deviation are normalized separately, eliminating the dimensional differences between different sub-objectives (e.g., power loss is in kW, switching operations are in the number of times), avoiding a single high-order objective from dominating the optimization result, and ensuring that all three objectives can participate in the decision-making process; then, through the allocation of weight coefficients, the actual governance needs of the distribution network can be considered (e.g., prioritizing voltage quality during peak electricity consumption, prioritizing equipment maintenance during peak periods, etc.). By flexibly adjusting the priorities of each objective (first reducing switching operations), this design overcomes the limitations of traditional fixed-objective optimization. The final weighted summation of the comprehensive objective function simultaneously covers three core requirements: economic operation of the power grid, equipment maintenance costs, and power quality. Specifically, calculating power loss by multiplying the square of branch current by its resistance allows for precise reduction of grid energy consumption, thus lowering operating costs. Controlling the number of operations by statistically analyzing the number of switch state changes reduces mechanical wear on switching equipment, extends its lifespan, and lowers maintenance costs. Optimizing voltage deviation by summing the absolute values ​​of the differences between node voltage and reference voltage directly improves power quality and prevents voltage exceedances from impacting user equipment. This design avoids the pitfalls of single-objective optimization (such as excessive switching operations in pursuit of loss reduction or neglecting energy consumption while prioritizing voltage protection) and flexibly adapts weights to different scenarios, ultimately achieving synergistic optimization of distribution network reconfiguration in terms of economy, reliability, and power quality. This solves the problems of objective conflict and result imbalance in traditional multi-objective optimization.

[0012] Furthermore, if the voltage value of the first-stage node exceeds the limit, based on the reconstructed network, the reconstructed network parameters, and the voltage value of the first-stage node, combined with a preset short-timescale distributed resource optimization configuration model, the distributed resource optimization configuration result and the voltage value of the second-stage node are obtained, specifically as follows: The first-stage node voltage value is compared with the preset node voltage acceptable range. If the first-stage node voltage value exceeds the node voltage acceptable range, it is determined that the first-stage node voltage value exceeds the limit. The reconstructed network, reconstructed network parameters, and first-stage node voltage values ​​are input into the short-timescale distributed resource optimization configuration model. The power flow constraints in the short-timescale distributed resource optimization configuration model are subjected to second-order cone relaxation processing, and binary variables are introduced to linearize the OLTC gate value and the capacitor bank capacity gate value to obtain the processed short-timescale distributed resource optimization configuration model. Based on the Gurobi solver, the processed short-timescale distributed resource optimization configuration model is solved to obtain the distributed resource optimization configuration results; wherein, the distributed resource optimization configuration results include the optimized adjustment level of OLTC, the optimized access capacity of capacitor banks, and the optimized active and reactive power output of photovoltaic and wind turbines. Based on the results of distributed resource optimization, the OLTC level, capacitor bank access capacity, and photovoltaic and wind turbine output are adjusted to obtain the adjusted distributed resources. Based on the adjusted operating status of the distributed resources, the node voltage value in the second stage is calculated. The short-timescale distributed resource optimization configuration model includes a minimum power loss objective function and constraints, including power flow constraints, node voltage constraints, OLTC operation constraints, capacitor bank operation constraints, and photovoltaic and wind turbine operation constraints.

[0013] This application accurately identifies voltage exceedance issues that persist after refactoring by comparing the first-stage node voltage values ​​with acceptable ranges, achieving seamless integration with the first-stage remediation and avoiding repetitive operations on already resolved issues, thus improving remediation efficiency. Next, the refactored network parameters are input into a short-timescale distributed resource optimization model. Second-order cone relaxation is used to handle power flow constraints, eliminating nonlinear solution challenges. Binary variables are introduced to linearize OLTC and capacitor bank tap values, ensuring efficient model solution (relying on the Gurobi solver for rapid result output) while also aligning with the discrete operating characteristics of the equipment, avoiding a disconnect between theoretical solutions and actual operation. The resulting optimization results cover OLTC tap values, capacitor bank capacity, and photovoltaic... Wind turbine output enables coordinated regulation of multiple types of distributed resources. OLTC precisely changes voltage levels through gear adjustment, capacitor banks suppress voltage fluctuations through reactive power compensation, and photovoltaic and wind turbine outputs are optimized to meet load demands. The synergistic effect of these three elements is more effective in addressing voltage issues than single-resource regulation. Finally, based on the optimization results, the resource operating status is adjusted, and the node voltage values ​​for the second stage are calculated. This not only further improves voltage quality based on the first-stage network reconfiguration (e.g., reducing voltage deviation and eliminating residual over-limits), but also reduces grid energy consumption based on the model's "minimum power loss" target. Simultaneously, constraints such as node voltage and equipment operation ensure regulatory safety (e.g., avoiding frequent OLTC gear changes and capacitor bank over-compensation). This process solves the problems of "limited effectiveness of single-resource operation, difficulty in model solving, and disconnection from previous topology optimization" in traditional distributed resource regulation. It achieves a progressive effect of "precisely locating residual problems - efficiently coordinating resource regulation - safely reducing losses and improving voltage," laying a better voltage foundation for the subsequent third-stage mobile energy storage dispatch.

[0014] Furthermore, if the voltage value of the second-stage node exceeds the limit, based on the distributed resource optimization configuration result and the voltage value of the second-stage node, combined with the preset long-term mobile energy storage scheduling and operation model, the long-term mobile energy storage scheduling path, the mobile energy storage charging and discharging state switching strategy, and the third-stage node voltage value are obtained, specifically as follows: The second-stage node voltage value is compared with the preset node voltage acceptable range. If the second-stage node voltage value exceeds the node voltage acceptable range, it is determined that the second-stage node voltage value exceeds the limit. The traffic network in the area where the power distribution network is located is modeled, and the network is abstracted as an undirected graph; the undirected graph includes a set of network nodes and a set of network edges; Based on the undirected graph, the connection relationship between road network nodes and edges is described by the adjacency matrix, the travel path and corresponding traffic time between each road network node are determined, and a complete road network model is obtained. The results of distributed resource optimization, the voltage values ​​of the second-stage nodes, and the complete road network model are input into the long-term mobile energy storage scheduling and operation model. The long-term mobile energy storage scheduling and operation model is constructed as a mixed integer linear programming model. Combined with the Gurobi solver, the target node of the mobile energy storage, the mobile energy storage scheduling path from the initial position to the target node, the charging and discharging status and charging and discharging power of the mobile energy storage at each time period are obtained. Based on the charging and discharging states and charging and discharging power of mobile energy storage at different times, a switching strategy for the charging and discharging states of mobile energy storage is obtained. Based on the mobile energy storage charging and discharging state switching strategy and combined with the real-time power flow distribution of the distribution network, the node voltage value of the third stage is calculated. The long-term mobile energy storage scheduling and operation model includes a minimum power loss objective function and constraints; the constraints include mobile energy storage scheduling constraints, mobile energy storage charging and discharging state switching constraints, and distribution network operation constraints.

[0015] This application accurately locates residual voltage exceedance issues after the first two short-term governance phases by comparing the second-stage node voltage values ​​with the acceptable range, achieving full-cycle coverage from "short-term governance to long-term completion," thus avoiding problems caused by a single time scale of governance methods. Next, it models the traffic network and determines travel paths and travel times using an adjacency matrix, combining "power system voltage governance" with "traffic network scheduling" across domains. This design breaks through the limitations of traditional approaches that only focus on power parameters, ensuring that mobile energy storage scheduling paths are feasible (e.g., avoiding traffic congestion and matching the timing requirements of grid voltage governance), avoiding a disconnect between theoretical scheduling and actual transportation. Subsequently, the network model and power parameters are input into a mixed-integer linear programming model, relying on G... The urobi solver obtains the target node, scheduling path, and charging / discharging parameters for each time period. It ensures economical grid operation by achieving the goal of "minimum power loss" while guaranteeing equipment safety (e.g., avoiding overcharging / over-discharging and scheduling timeouts) through mobile energy storage scheduling and charging / discharging constraints. Based on the switching strategy formed by the charging / discharging parameters, combined with real-time power flow calculations of the third-stage node voltage values, the spatiotemporal flexibility of mobile energy storage (on-demand scheduling to over-limit nodes and dynamic switching of charging / discharging states) can specifically address persistent voltage problems that are difficult to eliminate in the first two stages. For example, voltage can be boosted by discharging during peak load periods and reduced by charging during off-peak periods. This fills the gap in "persistent voltage control" in short-term timescale governance and further reduces grid losses through multi-time-period charging / discharging optimization. This process overcomes the shortcomings of traditional voltage governance, such as "single timescale, disconnect between resource scheduling and actual scenarios, and difficulty in resolving persistent voltage problems," ultimately achieving a breakthrough from "phased improvement" to "full-cycle compliance" of distribution network voltage, while simultaneously considering both economy and safety.

[0016] Furthermore, the voltage management of the distribution network based on the mobile energy storage scheduling path, charging / discharging state switching strategy, and third-stage node voltage value over a long period of time specifically involves: Based on the long-term mobile energy storage scheduling path, plan the transportation route and arrival time of mobile energy storage to the target node; Based on the transportation route of the mobile energy storage and the time of arrival at the target node, control the arrival of the mobile energy storage at the target node; After the mobile energy storage reaches the target node, according to the charging and discharging state switching strategy, combined with the third-stage node voltage value and the real-time operating status of the distribution network, the mobile energy storage is controlled to perform discharging operations at nodes that need voltage boosting and charging operations at nodes that need voltage debuffing. Real-time monitoring of voltage changes at each node of the distribution network during the charging and discharging process of mobile energy storage; if the node voltage values ​​in the third stage are all within the preset acceptable range, maintain the current mobile energy storage operation status until the voltage governance cycle ends. If node voltage exceeds the limit, adjust the charging and discharging power of the mobile energy storage according to the real-time monitored node voltage data until all node voltage values ​​are within the acceptable range, thus completing the voltage management of the distribution network.

[0017] This application first plans transportation routes and arrival times based on long-term mobile energy storage scheduling paths, transforming abstract scheduling schemes into implementable transportation plans. This avoids mobile energy storage missing voltage regulation windows due to unclear routes or misaligned timing, ensuring precise matching between regulation actions and grid demand. Next, it controls the mobile energy storage to arrive at the target node according to the plan, providing a "time-space dual-position" hardware foundation for subsequent voltage regulation, overcoming the limitations of traditional fixed energy storage that is "unable to move on demand and has limited coverage." After the energy storage arrives, it combines charging / discharging state switching strategies with the third-stage node voltage and real-time grid status, selectively discharging at boost nodes and charging at buck nodes. This operation relies on the results of previous model optimization to ensure accurate initial regulation direction and dynamically adjusts through "real-time operating status" to avoid regulation failures caused by deviations between model presets and actual operating conditions. Subsequently, by monitoring voltage changes in real time, it maintains operation at qualified voltages and fine-tunes charging / discharging power for voltage exceeding limits, forming a closed-loop control of "execution-monitoring-correction." This avoids energy storage losses and grid fluctuations caused by over-regulation and quickly eliminates occasional voltage exceeding limits, ensuring continuous qualified voltage throughout the regulation period. The entire process, from scheduling implementation to dynamic correction, progressively addresses the traditional problems of "scheduling not being implemented, control not being precise, and governance not being sustainable," ultimately transforming the distribution network voltage from "model optimization results" to "actual operational compliance," while also taking into account the efficiency of energy storage resource utilization and the stability of power grid supply.

[0018] Secondly, this application provides a voltage regulation device for a power distribution network, the voltage regulation device comprising: The acquisition module is used to acquire the initial system parameters and initial node voltage values ​​of the distribution network. The first processing module is used to obtain the reconstructed network, reconstructed network parameters, and first-stage node voltage values ​​based on the initial system parameters and the initial node voltage values, combined with a preset short-timescale distribution network reconstructing model, if the initial node voltage value exceeds the limit; wherein, the short-timescale distribution network reconstructing model is constructed and solved based on the targets of minimum power loss, minimum number of switching operations, and minimum voltage deviation. The second processing module is used to obtain the distributed resource optimization configuration result and the second-stage node voltage value based on the reconstructed network, the reconstructed network parameters, and the first-stage node voltage value, combined with a preset short-timescale distributed resource optimization configuration model, if the first-stage node voltage value exceeds the limit; wherein, the short-timescale distributed resource optimization configuration model is constructed and solved based on the minimum power loss target, combined with OLTC, capacitor bank, photovoltaic and wind turbine; The third processing module is used to, if the voltage value of the second-stage node exceeds the limit, obtain the mobile energy storage scheduling path, the mobile energy storage charging and discharging state switching strategy, and the third-stage node voltage value in the long-term dimension based on the distributed resource optimization configuration result and the voltage value of the second-stage node, combined with the preset long-term mobile energy storage scheduling and operation model; wherein, the long-term mobile energy storage scheduling and operation model is constructed and solved based on road network modeling and the minimum power loss target; The governance module is used to govern the voltage of the distribution network based on the mobile energy storage scheduling path, charging and discharging state switching strategy and the third-stage node voltage value under the long-term dimension.

[0019] The acquisition module of this application accurately collects initial system parameters and node voltage values, providing complete and accurate basic data for subsequent governance, avoiding deviations in governance direction due to data loss or errors, and laying the foundation for accurate governance. Next, the first processing module, addressing the initial voltage exceedance problem, outputs reconstruction results based on a short-timescale distribution network reconfiguration model with multi-objective optimization. This not only improves voltage distribution by optimizing network topology but also reduces losses and switching operations, achieving initial governance with a "structure optimization priority" approach, thus solving the problem of low efficiency in traditional single-method governance. Subsequently, the second processing module inherits the results from the first stage and, through... The multi-distributed resource collaborative optimization model further regulates voltage, utilizing the complementary characteristics of OLTC, capacitor banks, photovoltaics, and wind turbines to deepen voltage improvement effects in a short timescale, filling the gaps in residual voltage that are difficult to eliminate by topology reconfiguration alone. The third processing module addresses voltage exceedances that remain unresolved in the first two stages. Relying on a long-term mobile energy storage model based on road network modeling, it achieves "finalization" governance with flexible spatiotemporal scheduling and charging / discharging strategies, overcoming the limitations of short-term methods in continuous voltage control. Finally, the governance module translates the scheduling and strategies of the third stage into actual governance actions, ensuring the optimization results are implemented. The entire device forms a closed loop from data input to actual governance through modular collaboration of "acquisition-initial treatment-deepening-finalization-implementation," achieving precise matching of governance objectives at each stage while avoiding governance gaps caused by module disconnection. Ultimately, it solves the shortcomings of traditional devices such as "single governance methods, fragmented timescales, and disconnect between optimization and execution," achieving efficient, continuous, and precise governance of distribution network voltage.

[0020] Thirdly, this application provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the voltage management method for a power distribution network as described above. Its beneficial effects are the same as those of the voltage management method for a power distribution network provided in the first aspect of this application.

[0021] Fourthly, this application provides a terminal device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement any of the voltage management methods for a power distribution network as described in the first aspect. Attached Figure Description

[0022] Figure 1 : A schematic flowchart of an embodiment of the voltage management method for a power distribution network provided in this application; Figure 2 : A schematic diagram of an embodiment of the three-stage voltage management provided in this application; Figure 3: A schematic diagram of an embodiment of the voltage management device for a power distribution network provided in this application. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 Please refer to Figure 1 In order to solve the problem that existing technologies cannot accurately and efficiently manage the voltage of distribution networks, this invention provides a voltage management method for distribution networks, including steps S01-S05.

[0025] S01: Obtain the initial system parameters and initial node voltage values ​​of the distribution network.

[0026] In a preferred embodiment of this invention, the acquisition of the initial system parameters and initial node voltage values ​​of the distribution network specifically includes: To acquire initial system parameters for the distribution network, core parameters related to distribution network operation are collected synchronously through existing monitoring systems and data acquisition terminals. Specifically, the resistance and reactance parameters of each branch are derived by retrieving information on the branch conductor type, length, and material from distribution network design drawings and equipment ledgers, combined with commonly used power system parameter calculation standards. The active and reactive power loads at each node are collected in real-time by smart meters and load monitoring devices deployed at each power consumption node, capturing real-time power data from various loads such as residential, industrial, and commercial operations. The initial opening and closing status of sectionalizing switches and tie switches is determined by acquiring the switch opening and closing signals through switch status monitoring units, clarifying the current operating status of each switch. The initial adjustment level of the OLTC and the initial connected capacity of the capacitor bank are read through the local monitoring interface of the OLTC controller and capacitor bank control cabinet, ensuring that the acquired equipment operating parameters are consistent with actual operating conditions. The initial active and reactive power outputs of photovoltaic and wind turbines are recorded by collecting real-time power data from the photovoltaic inverters and wind turbine converters through the new energy power generation monitoring system, completing the recording of the initial state of new energy output.

[0027] To obtain the initial node voltage values, voltage monitoring devices are deployed at key nodes of the distribution network (including load concentration nodes, new energy grid connection points, and line branch nodes). These devices collect the three-phase voltage values ​​of each node in real time according to a preset collection frequency (e.g., once per second) and transmit the collected voltage data to the distribution network data processing center via the power communication network. The data processing center preprocesses the received voltage data, eliminating abnormal fluctuations caused by communication interference or temporary equipment failures to ensure the accuracy of the initial node voltage values. At the same time, the preprocessed voltage data of each node is initially compared with the node voltage acceptable range specified in the distribution network design specifications, providing a basis for subsequent judgment on whether to initiate the voltage management process.

[0028] The initial system parameters and initial node voltage values ​​obtained through the above methods need to be stored in the distribution network data storage module to form a structured initial dataset. This ensures that the required data can be quickly read when the distribution network reconfiguration model, distributed resource optimization configuration model, and long-term mobile energy storage scheduling and operation model are called. This provides a reliable data foundation for solving the models at each stage and formulating voltage management strategies.

[0029] S02: If the initial node voltage value exceeds the limit, based on the initial system parameters and the initial node voltage value, combined with the preset short-time scale distribution network reconfiguration model, the reconfigured network, reconfigured network parameters, and first-stage node voltage values ​​are obtained; wherein, the short-time scale distribution network reconfiguration model is constructed and solved based on the objectives of minimum power loss, minimum number of switching operations, and minimum voltage deviation.

[0030] In a preferred embodiment of this invention, if the initial node voltage value exceeds the limit, based on the initial system parameters and the initial node voltage value, and combined with a preset short-timescale distribution network reconfiguration model, the reconfigured network, reconfigured network parameters, and the first-stage node voltage value are obtained, specifically as follows: After collecting the initial system parameters and initial node voltage values ​​of the distribution network, the initial voltage status of each node is first determined: the preprocessed initial node voltage values ​​are compared one by one with the acceptable range of node voltages specified in the distribution network operation procedure. If the initial voltage value of any node exceeds the acceptable range (including excessively high or low voltage), it is determined that the initial node voltage has exceeded the limit, and the first stage of the distribution network reconfiguration process needs to be initiated to improve the voltage status.

[0031] When initiating the reconfiguration process, the initial system parameters (including branch resistance and reactance, node load power, initial switch states, etc.) and initial node voltage values ​​obtained in the early stages are imported into a pre-defined short-timescale distribution network reconfiguration model as input data. The core design objective of this model is multi-dimensional collaborative optimization, specifically covering three key directions: first, minimizing distribution network power loss by optimizing network topology to reduce energy loss during transmission; second, minimizing the number of switching operations to reduce equipment mechanical losses and maintenance costs caused by frequent switching actions; and third, minimizing voltage deviation to ensure that the voltage of each node after reconfiguration is as close as possible to the reference voltage, thereby improving power supply quality. To achieve these objectives, the model incorporates various constraints, including power flow constraints to ensure safe grid operation (ensuring power transmission meets equipment carrying capacity), network topology constraints to maintain the radial structure of the distribution network (avoiding operational risks caused by closed loops), switch state constraints to restrict switch action logic (preventing invalid or illegal switching operations), and network security operation constraints to ensure that voltage and current remain within safe ranges.

[0032] During the model solution process, the nonlinear constraints in the model (such as the quadratic terms in power flow calculation) are first subjected to second-order cone relaxation treatment to transform the nonlinear model into a convex optimization model that can be solved efficiently, avoiding the solution difficulties or local optimum problems caused by nonlinear characteristics. Then, the Gurobi solver is called to solve the relaxed model. During the solution process, the optimal state combination of distribution network sectional switches and tie switches is calculated by combining the input initial system parameters and voltage data, that is, the switch state optimization result.

[0033] Based on the optimized switch states obtained from the solution, the opening and closing states of the corresponding sectionalizing switches and tie switches in the distribution network are adjusted: for example, the sectionalizing switches of some overloaded branches are disconnected, and the preset tie switches are closed to switch the load power supply path, ultimately forming the reconstructed distribution network topology. Based on this reconstructed network topology, the operating parameters of the distribution network are recalculated using power flow calculation tools to obtain the reconstructed network parameters, specifically including the real-time current and power transmission values ​​of each branch, as well as the voltage values ​​of each node (i.e., the first-stage node voltage values).

[0034] Furthermore, the short-timescale distribution network reconfiguration model in this embodiment is specifically as follows: This embodiment comprehensively considers network loss, voltage quality, and the number of switching operations, with the overall objective function... It can be represented by equation (1). It contains three sub-objective functions, namely, minimum power loss. Minimum number of switching operations The minimum voltage deviation is represented by equations (2) and (3). Equation (5) normalizes the sub-objective function.

[0035] (1) In the formula, For the first The importance of each sub-function The larger the value, the more important the sub-function is during the optimization process; To optimize the first The normalized value of the subfunction.

[0036] 1) Maximum load recovery amount: (2) In the formula, For distribution network branch collection; branch road The square of the current; branch road The resistance.

[0037] 2) Minimum number of switching operations: (3) In the formula, For distribution network branch collection; This represents the switching action relative to the original switch state. A value of 1 indicates an operation, and a value of 0 indicates no operation.

[0038] 3) Minimum voltage deviation: (4) In the formula, The current node voltage; This is the reference voltage.

[0039] 4) Sub-objective function normalization: (5) In the formula, For the first The maximum value of the objective value of each sub-function.

[0040] (2) Constraints: The reconstructed network must meet the basic requirements for the safe and stable operation of the power system, including power flow constraints, node voltage constraints, branch capacity constraints, network structure constraints, and switch state constraints.

[0041] Equations (6)-(9) are the nodal power balance formulas. Equation (10) is the branch power balance formula. Current constraint; Equation (11) is for the branch Voltage constraint at both ends; Equations (12) and (13) indicate the branch voltage when the switch is opened. The active and reactive power transmitted are zero.

[0042] 1) Current constraints: (6) (7) (8) (9) (10) (11) (12) (13) In the formula, and For nodes The injected active and reactive power; and They are respectively from nodes To the node Transmitted active and reactive power; , and , Branch roads and Resistance and reactance; and They are respectively with nodes The set of connected parent nodes (upstream nodes) and the set of child nodes (downstream nodes); and They are located at the current node The active and reactive power output of the distributed generation (DG), if the node If DG is not connected and All are zero. It is a sufficiently large integer; and They are nodes Active power load and reactive power load; branch road The connected state variables; 2) Network security operation constraints: Equation (14) is the node voltage constraint; Equation (15) is the branch current upper limit constraint.

[0043] (14) (15) In the formula, and They are nodes Voltage upper and lower limits; branch road Current on; The upper limit of the current.

[0044] 3) Network radial constraints: The operation mode of the power grid is adjusted by changing the opening and closing state of the switches in the power system, that is, changing the original network topology. However, it is necessary to ensure that the reconstructed network meets the requirements of the radial structure topology. In graph theory, the distribution network conforms to the tree structure. The tree has the following characteristics: (1) all nodes in the tree are connected; (2) there is only one path between any two points in the tree; (3) there are no loops in the tree.

[0045] Equation (16) indicates that the depth of the substation node is 0; Equation (17) limits the range of node depth; Equations (18) and (19) ensure that the node depth increases unidirectionally from the parent node to the child node to eliminate loops, and constrain the uniqueness of the path between nodes while ensuring network connectivity.

[0046] (16) (17) (18) (19) In the formula, The number of network nodes; branch road The connectivity state variable has a value of 1 when the branch is normally connected and 0 otherwise; M is a sufficiently large integer. For depth constraints, auxiliary variables are defined if and only if the branch During normal connection The value is 1 if it is 1, otherwise it is 0.

[0047] 4) Switch state constraints: Equation (20) is the state constraint for the switch action.

[0048] (20) In the formula, For distribution network branch collection; branch road The switch action variable is as follows: since the sectionalizing switch is normally open and the connecting switch is normally closed, for the sectionalizing switch, a value of 1 indicates that the branch is open and vice versa. For the connecting switch, a value of 1 indicates that the branch is closed and vice versa. branch road The connected state variables; branch road The initial connectivity state variables.

[0049] Furthermore, the solution process for the short-timescale distribution network reconfiguration model in this embodiment is as follows: The aforementioned distribution network reconfiguration model contains quadratic constraints and is a nonlinear programming problem that cannot be solved directly. Therefore, it is necessary to relax the model.

[0050] By applying new variables to the voltage square term and current square term in the model respectively. and By performing variable substitution, equations (2), (7), (10), (11), (14), and (15) become: After relaxing the model, the solution is obtained through the Gurobi solver in the MATLAB / YALMIP interface, yielding the switching operation results and the optimized and reconstructed network.

[0051] Through the above process, not only can the initial voltage limit problem be directly improved by optimizing and adjusting the network topology, but also a better network foundation can be provided for the subsequent second-stage distributed resource optimization configuration while reducing power loss and switching operations, ensuring the continuity and efficiency of the entire voltage management process.

[0052] S03: If the voltage value of the first-stage node exceeds the limit, based on the reconstructed network, the reconstructed network parameters, and the voltage value of the first-stage node, combined with the preset short-timescale distributed resource optimization configuration model, the distributed resource optimization configuration result and the voltage value of the second-stage node are obtained; wherein, the short-timescale distributed resource optimization configuration model is based on the minimum power loss target and is constructed and solved by combining OLTC, capacitor bank, photovoltaic and wind turbine.

[0053] In a preferred embodiment of this example, if the voltage value of the first-stage node exceeds the limit, based on the reconstructed network, the reconstructed network parameters, and the voltage value of the first-stage node, combined with a preset short-timescale distributed resource optimization configuration model, the distributed resource optimization configuration result and the voltage value of the second-stage node are obtained, specifically as follows: After completing the first phase of distribution network reconfiguration and obtaining the first phase node voltage values, a second assessment of the reconfigured voltage status is required: the first phase node voltage values ​​of each node are compared one by one with the acceptable range of node voltages specified in the distribution network operation procedures. If there are still node voltage values ​​that exceed the acceptable range (such as some load-intensive nodes or new energy grid connection points where the voltage is still too high / too low), it is determined that the first phase node voltage has exceeded the limit, and the second phase of distributed resource optimization and allocation process needs to be initiated to further enhance the voltage management effect.

[0054] When initiating this process, the reconstructed distribution network topology, reconstructed network parameters (including the current, power, and node base voltage of each branch after reconstruction), and the node voltage values ​​of the first stage are used as core input data and imported into a preset short-timescale distributed resource optimization allocation model. This model aims to minimize power loss in the distribution network while ensuring the safety and feasibility of regulation through various constraints. Specific constraints include: power flow constraints to maintain power transmission balance; node voltage constraints to limit node voltage within a safe range; OLTC operation constraints to regulate the adjustment range and operating frequency of OLTC tap changers; capacitor bank operation constraints to control the reactive power compensation capacity of capacitor banks; and new energy operation constraints to match the output characteristics of photovoltaic and wind turbines.

[0055] In the model solving stage, firstly, second-order cone relaxation is applied to the power flow constraints in the model to eliminate the impact of nonlinear terms on the solution efficiency. For discrete variables such as OLTC tap settings and capacitor bank capacity tap settings, binary variables are introduced for linearization, transforming discrete constraints into linear constraints that the model can directly solve. This ensures that the solution results not only closely reflect the actual operating characteristics of the equipment but can also be quickly obtained through numerical calculation. Subsequently, the Gurobi solver is used to solve the processed model. Combining the input reconstructed network data and the first-stage voltage information, the optimal operating parameters for various types of distributed resources are calculated, i.e., the distributed resource optimization configuration results. Specifically, this includes the optimized adjustment tap of OLTC, the optimized access capacity of capacitor banks, and the optimized active and reactive power output of photovoltaic and wind turbines.

[0056] Based on the above optimization results, the operating status of distributed resources in the distribution network is adjusted: transformer tap changes are implemented according to the OLTC optimized tap position to alter the voltage ratio and adapt to node voltage demands; capacitors of corresponding capacity are added based on the optimized capacitor bank connection capacity to supplement or absorb reactive power and smooth voltage fluctuations; and the active and reactive power output ratios of renewable energy generation equipment are controlled via inverters based on the optimized output values ​​of photovoltaic and wind turbines, ensuring that renewable energy output better matches the distribution network load and voltage status. After all distributed resources are adjusted, the voltage of each node in the distribution network is recalculated using power flow calculation tools based on the adjusted resource operating status, yielding the adjusted node voltage values, i.e., the second-stage node voltage values.

[0057] Furthermore, the construction process of the short-timescale distributed resource optimization and allocation model in this embodiment is as follows: Dispatch OLTC, capacitor banks, photovoltaic and wind turbines to participate in voltage regulation with the goal of minimizing power loss in the distribution network.

[0058] (27) In the formula, For the set of branches after the distribution network reconfiguration; branch road The square of the current; branch road The resistance.

[0059] (2) Constraints: 1) Current constraints: In the formula, and For nodes The injected active and reactive power; and They are respectively from nodes To the node Transmitted active and reactive power; , and , Branch roads and Resistance and reactance; and They are respectively with nodes The set of connected parent nodes (upstream nodes) and the set of child nodes (downstream nodes); and They are nodes Active power load and reactive power load; and They are nodes The active power generated by photovoltaic and wind turbines; and They are nodes The reactive power generated by photovoltaic and wind turbines; For nodes The reactive power of the capacitor bank. In the distributed flexible resource allocation model, the power flow constraint is still non-convex, so the second-order cone relaxation in Step 1 is still used, as shown in equations (24)-(26).

[0060] 2) Node voltage constraints: OLTC can adjust the transformer tap position according to the current operating status of the distribution network, which can suppress voltage fluctuations and keep the node voltage within a safe range. The range constraint of the node voltage is shown in equation (23).

[0061] The voltage change expression for OLTC gear adjustment is shown in (30).

[0062] (30) In the formula, This represents the tap position of the transformer and is an integer variable. This is the primary voltage of the transformer; This represents the change in gear position for each adjustment.

[0063] 3) OLTC and capacitor bank operating constraints: Both OLTC and capacitor bank are discrete motion devices. The range of OLTC gear changes is shown in equation (31), and the capacity of capacitor bank is calculated as shown in equation (32).

[0064] (31) (32) In the formula, This represents the maximum value of the OLTC gear. The capacitor is connected at the node The reactive capacity of the capacitor at that location; This represents the threshold value for the reactive power capacity of the capacitor, and is an integer variable. This refers to the reactive power capacity corresponding to each change in capacitor position. This represents the maximum reactive power capacity of the capacitor.

[0065] 4) Constraints on photovoltaic and wind turbine operation: (33) (34) In the formula, and Connected to the nodes respectively Apparent power values ​​of photovoltaic and wind turbine inverters; and They are nodes The active power generated by photovoltaic and wind turbines; and They are nodes The reactive power generated by photovoltaic and wind turbines.

[0066] In the distributed resource optimization model, power flow constraints, voltage and current constraints can be relaxed using the previously mentioned methods to perform second-order cone relaxation on the nonlinear constraints. In the operational constraints of the OLTC and capacitor bank, the OLTC and capacitor bank speed limits are integer variables and require further linearization.

[0067] like And there are binary variables As shown in equation (35): (35) So This can be expressed as equation (36): (36) Similarly, we can obtain binary variables. And satisfying equation (37), the expression for the reactive power capacity of the capacitor is equation (38): (37) (38) After second-order cone relaxation and linearization, the distributed flexible resource optimization model can be solved using the commercial solver Gurobi.

[0068] This process can fully leverage the coordinated control capabilities of OLTC, capacitor banks, photovoltaic and wind turbines on the basis of the first-stage network reconfiguration, and specifically address the voltage over-limit issues that remain after reconfiguration. At the same time, it can reduce grid operating energy consumption with the goal of minimizing power loss, laying a better voltage foundation for the subsequent third-stage mobile energy storage dispatch if needed, and ensuring the progressive effect of voltage management.

[0069] S04: If the voltage value of the second-stage node exceeds the limit, based on the distributed resource optimization configuration result and the voltage value of the second-stage node, combined with the preset long-term mobile energy storage scheduling and operation model, the long-term mobile energy storage scheduling path, the mobile energy storage charging and discharging state switching strategy and the voltage value of the third-stage node are obtained; wherein, the long-term mobile energy storage scheduling and operation model is constructed and solved based on road network modeling and the minimum power loss target.

[0070] In a preferred embodiment of this invention, if the voltage value of the second-stage node exceeds the limit, based on the distributed resource optimization configuration result and the voltage value of the second-stage node, and combined with a preset long-term mobile energy storage scheduling and operation model, the long-term mobile energy storage scheduling path, the mobile energy storage charging and discharging state switching strategy, and the voltage value of the third-stage node are obtained, specifically as follows: After completing the second phase of distributed resource optimization and obtaining the second phase node voltage values, a third determination of the current voltage status is required: compare the second phase node voltage values ​​of each node with the acceptable range of node voltages specified in the distribution network operation procedures. If there are still node voltage values ​​that exceed the acceptable range (such as some node voltages being too low during periods of continuous high load, or voltages being too high during periods of high renewable energy generation), it is determined that the second phase node voltages have exceeded the limits, and the third phase of long-term mobile energy storage scheduling and operation process needs to be initiated to achieve the finalization of voltage management through the spatiotemporal flexibility of mobile energy storage.

[0071] When initiating this process, the first step is to model the traffic network in the area where the power distribution network is located: the actual traffic network is abstracted into an undirected graph containing network nodes (such as substation locations, energy storage sites, and road intersections) and network edges (such as roads connecting the nodes). The connection relationship between network nodes and edges is described by an adjacency matrix, and all travel paths between any two network nodes are identified. Combined with road traffic data and historical travel time statistics provided by the transportation department, the traffic time corresponding to each travel path is determined, and finally a complete network model is formed, providing a feasible path basis for the actual scheduling of mobile energy storage.

[0072] Subsequently, the results of the second-stage distributed resource optimization configuration (including OLTC settings, capacitor bank capacity, and photovoltaic and wind turbine output), the second-stage node voltage values, and the aforementioned complete road network model were used as input data and imported into a pre-defined long-term mobile energy storage scheduling and operation model. This model aims to minimize power loss in the distribution network and sets three key constraints: first, mobile energy storage scheduling constraints, limiting the location, scheduling path, and duration of mobile energy storage in each time period to ensure that the scheduling plan conforms to the actual traffic network; second, mobile energy storage charging and discharging state switching constraints, standardizing the charging and discharging power, state of charge range, and state switching logic of energy storage to avoid overcharging and over-discharging of equipment; and third, distribution network operation constraints, ensuring that the power flow, voltage, and current of the distribution network remain within safe ranges after the mobile energy storage is connected.

[0073] During the model solving process, the model is constructed as a mixed-integer linear programming model. Integer variables describe discrete decisions such as the target node and charging / discharging state of mobile energy storage, while linear variables describe continuous parameters such as charging / discharging power and scheduling time. This ensures that the model can both reflect the discrete characteristics of actual operation and solve efficiently. Subsequently, the Gurobi solver is called to solve the model. Combining the input grid parameters and road network data, the target node of mobile energy storage (i.e., the voltage-limited node to be connected), the scheduling path from the initial location to the target node (matching the travel path in the road network model), and the charging / discharging state (charging or discharging) and charging / discharging power of mobile energy storage in each time period are calculated. This forms the mobile energy storage scheduling path in the long-term dimension, as well as the mobile energy storage charging / discharging state switching strategy based on the charging / discharging state and power in each time period.

[0074] Based on the obtained scheduling path and charging / discharging state switching strategy, the power flow calculation tool is used to simulate the operation state of the distribution network after the mobile energy storage is connected: the mobile energy storage is scheduled to the target node according to the scheduling path, and corresponding operations are performed in each time period according to the charging / discharging state switching strategy (such as discharging during the time period of low voltage node and charging during the time period of high voltage node). The voltage of each node is recalculated in combination with the real-time power flow distribution of the distribution network to obtain the node voltage value after the mobile energy storage participates in the regulation, that is, the node voltage value of the third stage.

[0075] Furthermore, the construction process of the long-term mobile energy storage scheduling and operation model in this embodiment is as follows: According to graph theory, the transportation network can be modeled as an undirected graph, as shown in equation (39).

[0076] (39) In the formula, Let be the set of nodes in an undirected graph; Let be the set of edges in an undirected graph.

[0077] Furthermore, the diagram can be... It can be represented by an adjacency matrix, as shown in equation (40).

[0078] (40) In the formula, Adjacency matrix The value of the element at the position is as shown in equation (41).

[0079] (41) In the formula, Let be the set of nodes in an undirected graph; Let be the set of edges in an undirected graph; and It is a node in the transportation network.

[0080] The objective function and constraints of the long-term mobile energy storage scheduling and operation model are as follows: (1) Objective function: The mobile energy storage optimization scheduling and operation model aims to minimize power loss.

[0081] (42) In the formula, For the set of branches after the distribution network reconfiguration; branch road The square of the current; branch road The resistance.

[0082] (2) Constraints: 1) Mobile energy storage dispatch constraints: Equation (43) indicates that mobile energy storage can only be charged or discharged at one charging / discharging node during each time period. Equation (44) indicates that mobile energy storage will be dispatched to the node. Equations (45) and (46) represent moving to a node. The time for mobile energy storage to be dispatched from its initial location to the node The travel time is only when the mobile energy storage reaches the charging and discharging node. The value is 1. Equations (47), (48), and (49) ensure that only the target node of the mobile energy storage is a node. And has reached the node hour The value is 1 only if it is 1, otherwise it is 0.

[0083] (43) (44) (45) (46) (47) (48) (49) In the formula, For mobile energy storage collection; This is a binary variable; a value of 1 indicates that the target node for mobile energy storage is a node. ; and It is a binary variable; For mobile energy storage during transportation time; The time it takes for mobile energy storage to reach the target node; 2) Operational constraints of mobile energy storage: Equation (50) represents the state of charge constraint of mobile energy storage. Equation (51) is the calculation method of state of charge of mobile energy storage. Equations (52) and (53) are the active and reactive power constraints when mobile energy storage is discharging; Equations (54) and (55) are the active and reactive power constraints when mobile energy storage is charging.

[0084] (50) (51) (52) (53) (54) (55) In the formula, The current state of charge of mobile energy storage; and These are the minimum and maximum limits of the battery capacity, respectively. and Mobile energy storage at nodes and time period The active discharge power and reactive discharge power; and Mobile energy storage at nodes and time period The active charging power and reactive charging power; and These are the maximum active and reactive discharge power of mobile energy storage, respectively. and These are the maximum active and reactive charging power of mobile energy storage, respectively. and These are discharge efficiency and charging efficiency, respectively.

[0085] 3) Distribution network operation constraints: The constraints of the distribution network also include equations (23)-(26).

[0086] Furthermore, the solution process for the long-term mobile energy storage scheduling and operation model is as follows: Based on the above objective function and constraints, the scheduling and operation of mobile energy storage is modeled as a mixed integer linear programming problem and solved using Gurobi. In the optimization results, the charging and discharging state of mobile energy storage will switch according to the operating state of the distribution network, thereby improving voltage quality.

[0087] This process leverages the long-term scheduling and dynamic charging / discharging capabilities of mobile energy storage to specifically address persistent and stubborn voltage exceedance issues that are difficult to eliminate in the short-term governance phases of the first two stages. At the same time, it aims to ensure the economical operation of the power grid with minimal power loss, achieving a breakthrough in distribution network voltage governance from "phased improvement" to "full-cycle compliance".

[0088] S05: Based on the mobile energy storage scheduling path, charging and discharging state switching strategy and third-stage node voltage value under the long-term dimension, the voltage of the distribution network is managed.

[0089] In a preferred embodiment of this invention, the voltage management of the distribution network based on the mobile energy storage scheduling path, charging / discharging state switching strategy, and third-stage node voltage value over a long period of time specifically involves: After obtaining the mobile energy storage dispatch path, charging and discharging state switching strategy, and third-stage node voltage values ​​over a long period, the optimization results need to be transformed into actual distribution network voltage management operations to ensure the voltage management effect is implemented. The specific execution process is as follows: First, for the mobile energy storage dispatch path, the dispatch path is dynamically fine-tuned based on real-time traffic information in the distribution network area (such as road conditions, temporary traffic control information, etc.) to form the final mobile energy storage transportation route. The specific time nodes for the mobile energy storage to depart from the initial parking site, pass through each road segment, and arrive at the target node are clearly defined, generating a visualized transportation dispatch plan. At the same time, the plan is synchronized to the mobile energy storage operation and maintenance team and the distribution network control center to ensure that the operation and maintenance personnel complete the mobile energy storage transfer preparation according to the plan, and the control center makes preparations for energy storage access at the target node in advance (such as checking the access interface and reserving access capacity).

[0090] Once the mobile energy storage arrives at the target node as planned, the maintenance personnel first complete the electrical connection between the mobile energy storage and the target node of the distribution network. Then, the control center, based on the charging and discharging state switching strategy and combined with the third-stage node voltage value and the real-time operating status of the distribution network (such as real-time load changes, fluctuations in renewable energy output, and the operating status of other distributed resources), sends charging and discharging control commands to the mobile energy storage: For nodes whose third-stage node voltage value is lower than the lower limit of the qualified range, the mobile energy storage is controlled to enter the discharging mode, increasing the node voltage by injecting active or reactive power into the distribution network; for nodes whose third-stage node voltage value is higher than the upper limit of the qualified range, the mobile energy storage is controlled to enter the charging mode, reducing the node voltage by absorbing active or reactive power from the distribution network, ensuring that the charging and discharging operation accurately matches the node voltage management needs.

[0091] During the charging and discharging operations of mobile energy storage, the distribution network control center collects and analyzes voltage change data of all nodes in the distribution network in real time through voltage monitoring devices deployed at each node, forming a voltage monitoring curve. If the monitoring finds that the node voltage values ​​in the third stage have all stabilized within the preset node voltage qualification range, and the duration meets the distribution network operation requirements (e.g., no fluctuation for 15 minutes), then the current charging and discharging power and operating mode of the mobile energy storage are maintained until the end of this voltage governance cycle (e.g., completing the voltage guarantee during the peak load period of the day). If the monitoring finds that there are still node voltage over-limit situations (e.g., some node voltages are low again due to a sudden increase in load), then the charging and discharging power of the mobile energy storage is dynamically adjusted according to the real-time monitored node voltage data (e.g., appropriately increasing the discharge power to enhance the voltage boosting effect) until all node voltage values ​​are stable within the qualification range, and finally the distribution network voltage governance work is completed.

[0092] Throughout the entire governance process, the dynamic adaptation of scheduling paths, precise control of charging and discharging operations, and real-time monitoring and correction of voltage have enabled the deep integration of mobile energy storage optimization strategies with the actual operating conditions of the distribution network. This ensures stable and reliable voltage governance results while avoiding the impact of excessive regulation on the operational stability of the distribution network and the lifespan of mobile energy storage devices.

[0093] In summary, as Figure 2 As shown, Figure 2 The flowchart illustrating the distribution network voltage management process presents the management logic in three stages from left to right: First, initial system parameters and node voltage values ​​are collected. If the voltage exceeds the limit, a short-term distribution network reconfiguration is performed to obtain the reconfigured network and the first-stage voltage value. If the voltage still exceeds the limit in the first stage, short-term distributed resource optimization is carried out, outputting the distributed resource optimization results and the second-stage voltage value. If the voltage still exceeds the limit in the second stage, long-term mobile energy storage scheduling and operation are initiated to obtain the mobile energy storage scheduling path, charging and discharging strategy, and the third-stage voltage value. Finally, the distribution network voltage management is completed based on these results. Each stage includes a closed-loop logic of data acquisition, model application, result output, and limit judgment.

[0094] This embodiment can quickly identify voltage exceedance situations by acquiring the initial system parameters and initial node voltage values ​​of the distribution network. When the initial node voltage value exceeds the limit, a short-timescale distribution network reconfiguration model based on the objectives of minimum power loss, minimum number of switching operations, and minimum voltage deviation is used to reconfigure the network, obtaining the reconfigured network and the first-stage node voltage values. This step can quickly adjust the network structure and initially alleviate the voltage exceedance problem. If the first-stage node voltage value still exceeds the limit, a short-timescale distributed resource optimization configuration model is further combined to construct an optimized configuration for OLTC, capacitor banks, photovoltaics, and wind turbines, obtaining the optimized configuration results and the second-stage node voltage values. This step further improves the voltage level by optimizing the configuration of distributed resources. If the second-stage node voltage value still exceeds the limit, a long-timescale mobile energy storage scheduling and operation model is finally combined to obtain the scheduling path of mobile energy storage, the charging and discharging state switching strategy, and the third-stage node voltage values. This step completely solves the voltage exceedance problem in the long-term dimension through the flexible scheduling and charging and discharging control of mobile energy storage. The entire process is progressive, comprehensively considering network structure, distributed resource allocation, and mobile energy storage scheduling. It not only effectively manages voltage exceedances but also reduces power loss, decreases the number of switching operations, and improves the overall operating efficiency and reliability of the distribution network. This application effectively solves the problem that existing technologies cannot accurately and efficiently manage voltage in distribution networks.

[0095] Example 2 Please refer to Figure 3 This is a voltage management device for a power distribution network provided in the embodiments of this application.

[0096] In this embodiment, the voltage management device for the power distribution network includes an acquisition module 10, a first processing module 20, a second processing module 30, a third processing module 40, and a management module 50.

[0097] The acquisition module 10 is used to acquire the initial system parameters and initial node voltage values ​​of the distribution network; The first processing module 20 is used to obtain the reconstructed network, reconstructed network parameters, and first-stage node voltage values ​​based on the initial system parameters and the initial node voltage values, combined with a preset short-timescale distribution network reconstructing model, if the initial node voltage value exceeds the limit; wherein, the short-timescale distribution network reconstructing model is constructed and solved based on the targets of minimum power loss, minimum number of switching operations, and minimum voltage deviation. The second processing module 30 is used to obtain the distributed resource optimization configuration result and the second-stage node voltage value based on the reconstructed network, the reconstructed network parameters, and the first-stage node voltage value, combined with a preset short-timescale distributed resource optimization configuration model, if the first-stage node voltage value exceeds the limit; wherein, the short-timescale distributed resource optimization configuration model is constructed and solved based on the minimum power loss target, combined with OLTC, capacitor bank, photovoltaic and wind turbine. The third processing module 40 is used to, if the voltage value of the second-stage node exceeds the limit, based on the distributed resource optimization configuration result and the voltage value of the second-stage node, and combined with the preset long-term mobile energy storage scheduling and operation model, obtain the long-term mobile energy storage scheduling path, the mobile energy storage charging and discharging state switching strategy, and the voltage value of the third-stage node; wherein, the long-term mobile energy storage scheduling and operation model is constructed and solved based on road network modeling and the minimum power loss target; The governance module 50 is used to govern the voltage of the distribution network based on the mobile energy storage scheduling path, charging and discharging state switching strategy and the third-stage node voltage value under the long-term dimension.

[0098] For ease of description and brevity, the embodiments of the device of the present invention include all the implementation methods in the above-described embodiments of the voltage management method for power distribution networks, and will not be repeated here.

[0099] Example 3: This application provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the voltage management method for a power distribution network. The voltage management method for a power distribution network, if implemented as a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented 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 the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0100] Example 4 This embodiment provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements any one of the voltage management methods for a power distribution network as described in Embodiment 1.

[0101] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for voltage management of a power distribution network, characterized by, The application relates to a voltage control method for a power distribution network. The method comprises the following steps: acquiring initial system parameters and initial node voltage values of the power distribution network; if the initial node voltage values are out of limits, based on the initial system parameters and the initial node voltage values, a short-time-scale power distribution network reconstruction model is combined to obtain a reconstructed network, reconstructed network parameters and first-stage node voltage values; the short-time-scale power distribution network reconstruction model is obtained based on minimum power loss, minimum switch operation number and minimum voltage deviation target; if the first-stage node voltage values are out of limits, based on the reconstructed network, the reconstructed network parameters and the first-stage node voltage values, a short-time-scale distributed resource optimization configuration model is combined to obtain distributed resource optimization configuration results and second-stage node voltage values; the short-time-scale distributed resource optimization configuration model is obtained based on a minimum power loss target, combined with OLTC, capacitor banks, photovoltaic and wind power; if the second-stage node voltage values are out of limits, based on the distributed resource optimization configuration results and the second-stage node voltage values, a long-time-scale mobile energy storage scheduling and operation model is combined to obtain a long-time-dimension mobile energy storage scheduling path, a mobile energy storage charging and discharging state switching strategy and third-stage node voltage values; the long-time-scale mobile energy storage scheduling and operation model is obtained based on road network modeling and a minimum power loss target; 2. The method of voltage governance of an electrical distribution network of claim 1, wherein, the voltage of the power distribution network is controlled according to the long-time-dimension mobile energy storage scheduling path, the charging and discharging state switching strategy and the third-stage node voltage values.

3. The method of voltage governance of an electrical distribution network of claim 1, wherein, The initial system parameters of the power distribution network include the resistance and reactance of each branch of the power distribution network, the active power load and the reactive power load of each node, the initial opening and closing state of sectional switches and tie switches, the initial adjustment gear of OLTC, the initial access capacity of capacitor banks and the initial active power output and the initial reactive power output of photovoltaic and wind power. If the initial node voltage values are out of limits, based on the initial system parameters and the initial node voltage values, a short-time-scale power distribution network reconstruction model is combined to obtain a reconstructed network, reconstructed network parameters and first-stage node voltage values, and the specific process is as follows: the initial node voltage values are compared with a preset node voltage qualified range, and if the initial node voltage values exceed the node voltage qualified range, it is determined that the initial node voltage values are out of limits; the initial system parameters and the initial node voltage values are input into the short-time-scale power distribution network reconstruction model, the non-linear constraints in the short-time-scale power distribution network reconstruction model are subjected to second-order cone relaxation processing, and the short-time-scale power distribution network reconstruction model after the second-order cone relaxation is solved by using a Gurobi solver to obtain switch state optimization results; the opening and closing states of sectional switches and tie switches of the power distribution network are adjusted according to the switch state optimization results to obtain a reconstructed network; based on the topological information of the reconstructed network, reconstructed network parameters are calculated, wherein the reconstructed network parameters include the current, power and first-stage node voltage values of each branch after reconstruction. The short-time-scale power distribution network reconstruction model comprises a comprehensive objective function and constraint conditions; the comprehensive objective function is a weighted summation function of minimum power loss, minimum switch operation number and minimum voltage deviation; and the constraint conditions comprise power flow constraints, network safe operation constraints, network radial constraints and switch state constraints.

4. The method of voltage governance of an electrical distribution network of claim 3, wherein, The comprehensive objective function is a weighted summation function of minimum power loss, minimum switch operation number and minimum voltage deviation, and is specifically: The minimum power loss sub-objective, the minimum switch operation number sub-objective and the minimum voltage deviation sub-objective are normalized respectively to obtain respective normalized sub-objectives; Weight coefficients are assigned to the respective normalized sub-objectives; According to the respective normalized sub-objectives and the corresponding weight coefficients, a comprehensive objective function of the short-time-scale power distribution network reconstruction model is obtained; The minimum power loss sub-objective is obtained by calculating the sum of the products of the square of all branch currents of the power distribution network and the branch resistance, the minimum switch operation number sub-objective is obtained by counting the number of switches that change relative to the initial switch state, and the minimum voltage deviation sub-objective is obtained by calculating the sum of the absolute values of the voltage differences between the node voltages and the reference voltage.

5. The method of voltage governance of an electrical distribution network of claim 1, wherein, If the first-stage node voltage value is out of limit, a distributed resource optimization configuration result and a second-stage node voltage value are obtained based on the reconstructed network, the reconstructed network parameters and the first-stage node voltage value, and in combination with a preset short-time-scale distributed resource optimization configuration model, and specifically: The first-stage node voltage value is compared with a preset node voltage qualified range, and if the first-stage node voltage value is out of the node voltage qualified range, it is determined that the first-stage node voltage value is out of limit; The reconstructed network, the reconstructed network parameters and the first-stage node voltage value are input into the short-time-scale distributed resource optimization configuration model, the power flow constraints in the short-time-scale distributed resource optimization configuration model are processed by second-order cone relaxation, and binary variables are introduced to linearize the tap value of the OLTC and the capacity tap value of the capacitor bank, so as to obtain a processed short-time-scale distributed resource optimization configuration model; The processed short-time-scale distributed resource optimization configuration model is solved based on a Gurobi solver, so as to obtain a distributed resource optimization configuration result; wherein the distributed resource optimization configuration result comprises an optimized adjustment tap of the OLTC, an optimized access capacity of the capacitor bank, and optimized active power output and reactive power output of the photovoltaic and wind turbine; According to the distributed resource optimization configuration result, the tap of the OLTC, the access capacity of the capacitor bank and the output of the photovoltaic and wind turbine are adjusted, so as to obtain adjusted distributed resources; Based on the operating state of the adjusted distributed resources, a second-stage node voltage value is calculated. The short-time-scale distributed resource optimization configuration model comprises a minimum power loss objective function and constraint conditions; the constraint conditions comprise power flow constraints, node voltage constraints, OLTC operation constraints, capacitor bank operation constraints and photovoltaic and wind turbine operation constraints.

6. The method of voltage governance of an electrical distribution network of claim 1, wherein, The second stage node voltage value is compared with the preset node voltage qualified range, and if the second stage node voltage value exceeds the node voltage qualified range, it is determined that the second stage node voltage value is out of limit. The traffic network of the region where the power distribution network is located is modeled, and the road network is abstracted as an undirected graph. The undirected graph includes a road network node set and a road network edge set. Based on the undirected graph, the connection relationship between road network nodes and edges is described through an adjacency matrix to determine the passing paths and corresponding traffic times between road network nodes, and a complete road network model is obtained. The distributed resource optimization configuration result, the second stage node voltage value and the complete road network model are input into the long time scale mobile energy storage scheduling and operation model, and the long time scale mobile energy storage scheduling and operation model is constructed as a mixed integer linear programming model, combined with a Gurobi solver, to obtain the target node of the mobile energy storage, the mobile energy storage scheduling path from the initial position to the target node, the charging and discharging state of the mobile energy storage in each period and the charging and discharging power. According to the charging and discharging state and the charging and discharging power of the mobile energy storage in each period, a mobile energy storage charging and discharging state switching strategy is obtained. Based on the mobile energy storage charging and discharging state switching strategy, combined with the real-time power flow distribution of the power distribution network, the third stage node voltage value is calculated. The long time scale mobile energy storage scheduling and operation model includes a minimum power loss objective function and constraint conditions; the constraint conditions include mobile energy storage scheduling constraints, mobile energy storage charging and discharging state switching constraints, and power distribution network operation constraints. According to the long time dimension mobile energy storage scheduling path, the charging and discharging state switching strategy and the third stage node voltage value, the voltage of the power distribution network is managed, specifically:

7. The method of voltage governance of an electrical distribution network of claim 1, wherein, According to the long time dimension mobile energy storage scheduling path, the transportation route and the time of the mobile energy storage to the target node are planned; According to the transportation route and the time of the mobile energy storage to the target node, the mobile energy storage is controlled to arrive at the target node; After the mobile energy storage arrives at the target node, according to the charging and discharging state switching strategy, combined with the third stage node voltage value and the real-time operation state of the power distribution network, the mobile energy storage is controlled to perform discharging operation at the node needing voltage rise and charging operation at the node needing voltage drop; The voltage of each node of the power distribution network during the charging and discharging process of the mobile energy storage is monitored in real time, and if all the third stage node voltage values are within the preset node voltage qualified range, the current mobile energy storage operation state is maintained until the voltage management period ends; If there are still nodes with voltage out of limit, the charging and discharging power of the mobile energy storage is adjusted according to the real-time monitored node voltage data until all the node voltage values are within the qualified range, and the voltage management of the power distribution network is completed. The acquisition module is configured to acquire initial system parameters and initial node voltage values of the power distribution network.

8. A voltage management device for a power distribution network, characterized by ​ ​ The first processing module is configured to, if the initial node voltage value is out of limit, obtain a reconstructed network, a reconstructed network parameter and a first-stage node voltage value based on the initial system parameter and the initial node voltage value and in combination with a preset short-time-scale power distribution network reconstruction model, wherein the short-time-scale power distribution network reconstruction model is obtained based on a minimum power loss, a minimum switch operation number and a minimum voltage deviation target. The second processing module is configured to, if the first-stage node voltage value is out of limit, obtain a distributed resource optimization configuration result and a second-stage node voltage value based on the reconstructed network, the reconstructed network parameter and the first-stage node voltage value and in combination with a preset short-time-scale distributed resource optimization configuration model, wherein the short-time-scale distributed resource optimization configuration model is obtained based on a minimum power loss target in combination with an OLTC, a capacitor bank, a photovoltaic device and a wind power device. The third processing module is configured to, if the second-stage node voltage value is out of limit, obtain a long-time-dimension mobile energy storage scheduling path, a mobile energy storage charging and discharging state switching strategy and a third-stage node voltage value based on the distributed resource optimization configuration result and the second-stage node voltage value and in combination with a preset long-time-scale mobile energy storage scheduling and operation model, wherein the long-time-scale mobile energy storage scheduling and operation model is obtained based on a road network modeling and a minimum power loss target. The management module is configured to manage voltage of the power distribution network according to the long-time-dimension mobile energy storage scheduling path, the charging and discharging state switching strategy and the third-stage node voltage value.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein the computer program controls a device in which the computer readable storage medium is located to perform the voltage management method of the power distribution network according to any one of claims 1 to 7 when the computer program is executed.

10. A terminal device, comprising: The computer readable storage medium comprises a stored computer program, wherein the computer program controls a device in which the computer readable storage medium is located to perform the voltage management method of the power distribution network according to any one of claims 1 to 7 when the computer program is executed. The computer readable storage medium comprises a stored computer program, wherein the computer program controls a device in which the computer readable storage medium is located to perform the voltage management method of the power distribution network according to any one of claims 1 to 7 when the computer program is executed.

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