Low-voltage transformer area micro-grid scheduling control method based on power distribution master station

Abstract

The invention discloses a low-voltage transformer area micro-grid scheduling control method based on a power distribution master station, which executes global centralized control by a power distribution automation master station system DMS, and comprises the following steps: establishing a low-voltage transformer area micro-grid model, collecting operation data in a micro-grid, and monitoring the operation state of the micro-grid in real time; when it is monitored that the micro-grid needs to operate in an off-grid mode or is in an off-grid state, an optimal scheduling strategy used for maintaining off-grid stability is issued to a micro-grid control system; when it is monitored that the micro-grid needs to execute grid-connected / off-grid mode switching, a mode switching preparation instruction is issued to a micro-grid control system firstly, then a switching control instruction is issued to a grid-connected switch, and mode switching is completed in a dominant mode; when it is detected that the micro-grid is separated from the power distribution network and is in a full-stop state, starting a black-start recovery process coordinated by a master station, and issuing a black-start instruction containing a recovery sequence to a micro-grid control system; and the execution effect of the instruction is evaluated, and if the preset requirement is not met, the scheduling strategy is dynamically adjusted and optimized to form closed-loop scheduling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatch automation, and in particular to a low-voltage area microgrid dispatch control method based on a power distribution master station. BACKGROUND

[0002] With the increasing penetration of photovoltaic, wind power and other distributed energy in low-voltage area microgrids, the intermittency and volatility of their output have brought significant challenges to microgrid operation. The output of such power sources is affected by natural factors such as weather and light, and there are obvious differences between peak and valley. Traditional microgrid dispatch relies on local controllers for local decision-making, and can only adjust the operating state of equipment based on limited local data, making it difficult to achieve multi-source collaborative optimization. For example, when photovoltaic output drops sharply, the local controller may not be able to quickly coordinate the discharge of energy storage and load reduction, leading to power imbalance within the microgrid, causing voltage and frequency fluctuations, and affecting power supply reliability. In addition, the dispersion of distributed energy increases the risk of local overload or power return, and the local controller lacks a holistic perspective of global load and power, and cannot adjust the operating strategy in advance.

[0003] The switching between grid-connected and off-grid modes is a key scenario in microgrid operation. The traditional switching mechanism relies on local controllers to independently complete state detection and instruction issuance. However, due to the lack of master station synchronization of global parameters on the distribution network side and the microgrid side, the switching process is prone to short power outages or power surges. It is known that precise matching of synchronization conditions such as voltage, frequency, and phase is required during switching. However, the local controller is limited by communication bandwidth and data acquisition capability, making it difficult to obtain real-time dynamic parameters of the distribution network side, which may lead to synchronization failure or power surges at the moment of closing, affecting user power experience and potentially causing damage to microgrid equipment. In addition, insufficient pre-regulation of source, load and storage devices during the switching process also exacerbates the risk of power fluctuations, such as the failure to adjust the state of energy storage to the appropriate interval in advance, resulting in the inability to quickly respond to power demand after switching.

[0004] The black start recovery capability of microgrids after complete power outage is an important link to ensure power supply continuity. Existing solutions rely on the independent start of local energy storage devices, but lack the coordinated control of the master station, making it difficult to guarantee recovery efficiency and reliability. Local energy storage has limited capacity, and the local controller cannot prioritize global loads and sequence the access of power sources, often resulting in key loads (such as medical facilities and communication base stations) being unable to recover first, while non-critical loads consume energy storage resources too early, prolonging the overall recovery time. At the same time, the gradual access of distributed power sources lacks the optimized dispatch of the master station, which may cause system instability due to mismatched output, affecting the success rate of black start. For example, the local controller may start non-critical loads first, causing the energy storage to be depleted too early and unable to support the subsequent grid connection of distributed power sources and the recovery of critical loads.

[0005] Traditional microgrid scheduling strategies are mostly based on static, preset rules, making it difficult to adapt to the dynamic changes in distributed energy resources and loads. Furthermore, the functional limitations of traditional Distribution Management Stations (DMS) further restrict the improvement of scheduling efficiency. Traditional DMSs primarily focus on data acquisition and status monitoring, lacking in-depth analysis and decision-making capabilities, and are unable to perform predictive scheduling and dynamic optimization based on real-time operational data. For example, when load demand suddenly increases, static scheduling strategies cannot adjust the output of distributed power sources and the charging and discharging plans of energy storage in a timely manner, leading to a decline in the economic efficiency of microgrid operation. Simultaneously, the lack of a closed-loop feedback mechanism means that scheduling strategies cannot adaptively adjust based on execution results, making it difficult to cope with complex and ever-changing microgrid operating scenarios. Moreover, the interaction between traditional DMSs and microgrid local controllers is limited to data upload, failing to achieve bidirectional coordination of scheduling commands and thus failing to fully leverage the global optimization capabilities of the master station. Summary of the Invention

[0006] To address the shortcomings and deficiencies of existing technologies, this invention provides a low-voltage distribution area microgrid scheduling and control method based on a distribution master station. The method uses the distribution automation master station system (DMS) as the global centralized control hub, replacing the traditional microgrid's decentralized decision-making mode that relies on local controllers, thereby achieving intelligent and efficient scheduling of microgrid operation.

[0007] This method first establishes a microgrid model covering power sources, loads, and energy storage devices within the low-voltage distribution area using a DMS (Digital Management System). It collects multi-source operational data, including power source data (photovoltaic power generation, wind power generation, diesel generator status, etc.), load data (electrical load of each low-voltage distribution area, priority of key loads, etc.), energy storage device data (battery state of charge, charging and discharging power, energy storage capacity, etc.), and grid operation data (distribution network voltage, frequency, power flow, etc.). The data is then exchanged in real time with the microgrid control system through a fusion terminal to monitor microgrid voltage stability, frequency fluctuations, power balance, and energy storage device status. An early warning mechanism is activated when an anomaly occurs.

[0008] When a microgrid needs to operate off-grid or is already off-grid, the DMS formulates and issues optimized scheduling strategies based on real-time data. These strategies include maintaining off-grid frequency and voltage stability by adjusting the output of photovoltaic inverters and energy storage converters and using droop control or virtual synchronous machine technology, as well as off-grid power scheduling strategies based on load priority (prioritizing critical loads), optimizing energy storage charging and discharging to avoid overcharging and over-discharging, and dynamically adjusting power allocation by combining model predictive control or artificial intelligence algorithms.

[0009] To address the switching requirements of microgrids between grid-connected and off-grid modes, the DMS first issues a preparation command to the microgrid control system. After the source-load-storage equipment has been pre-adjusted, a switching command is then issued to the grid-connected switch. The off-grid to grid-connected switching requires that the voltage difference, frequency difference, and phase difference on both sides be within a preset reasonable range and that the phase sequence be consistent. During the switching process, the DMS controls and displays the status of the switching process through the fusion terminal.

[0010] When the microgrid is disconnected from the distribution network and is in a state of complete shutdown, the DMS initiates a black start recovery process coordinated with the master station, controls the energy storage device to establish the microgrid voltage and frequency reference in voltage and frequency control mode, calculates the duration of energy storage output, guides distributed power sources to connect to the grid in an orderly manner according to a preset order (at least including the priority of photovoltaic and wind power access), restores critical loads first and then gradually connects general loads, and generates non-critical load disconnection instructions when necessary to ensure power balance.

[0011] In addition, the DMS evaluates the effectiveness of command execution, covering indicators such as voltage compliance rate, frequency deviation, active and reactive power balance deviation rate, and average outage duration of critical loads. Each indicator must meet preset standards (e.g., voltage compliance rate is measured by the ratio of voltage compliance time to total operating time). If the evaluation fails to meet the standards, the scheduling strategy is dynamically adjusted based on feedback, forming a closed-loop optimization. Furthermore, before issuing commands, the DMS can combine big data analysis and machine learning algorithms to predict the future operating status of the microgrid, ensuring that commands are adapted to actual needs.

[0012] The present invention also provides a scheduling and control system (including DMS, microgrid control system, grid-connected switch and execution equipment) and computer equipment for implementing the above method, which effectively solves the problems of insufficient global optimization capability, easy power loss during mode switching and slow recovery during black start in the prior art, and significantly improves the reliability and economy of low-voltage distribution area microgrid operation.

[0013] The present invention specifically adopts the following technical solution: A low-voltage distribution area microgrid dispatching and control method based on a distribution master station, wherein the distribution automation master station system (DMS) performs global centralized control, including: A low-voltage distribution area microgrid model is established based on DMS, and the operation data within the microgrid is collected and the operation status of the microgrid is monitored in real time. When it is detected that the microgrid needs to be disconnected from the network or is already in a disconnected state, the DMS formulates and issues an optimized scheduling strategy to the microgrid control system based on the operation data to maintain disconnection stability. When the microgrid is detected to need to perform grid-connected / off-grid mode switching, the DMS first sends a mode switching preparation command to the microgrid control system. After the microgrid control system completes the pre-adjustment of the microgrid's internal source load storage equipment, it then sends a switching control command to the grid-connected switch to complete the mode switching. When the microgrid is detected to be disconnected from the distribution network and in a state of complete shutdown, the DMS initiates a black start recovery process coordinated by the master station, sends a black start command containing the recovery sequence to the microgrid control system, and takes the lead in gradually restoring power supply starting from the energy storage device; The DMS evaluates the execution effect of the above instructions. If the preset requirements are not met, the scheduling strategy is dynamically adjusted and optimized to form a closed-loop scheduling.

[0014] Furthermore, the microgrid's operational data includes power supply-side data, load-side data, energy storage device data, and grid operation data; among which, power supply-side data includes photovoltaic power generation, wind power generation, and diesel generator status; load-side data includes the power load of each low-voltage distribution area and the priority of critical loads; energy storage device data includes battery state of charge, charging and discharging power, and energy storage capacity; and grid operation data includes distribution network voltage, frequency, and power flow. The real-time monitoring of microgrid operation status includes voltage stability, frequency fluctuation, power balance status, matching degree between distributed power output and load demand, and charging and discharging status and remaining capacity of energy storage devices; if voltage exceeds the limit or frequency fluctuation is detected, the DMS will activate the early warning mechanism.

[0015] Furthermore, the optimized scheduling strategy for maintaining off-grid stability includes: an off-grid frequency and voltage scheduling strategy and an off-grid power scheduling strategy. Off-grid frequency and voltage scheduling strategy: The DMS collects the frequency and voltage of the low-voltage AC side of the distribution area. When the frequency is lower than the safety threshold, an alarm is automatically generated. The system frequency and voltage are maintained by adjusting the output of the photovoltaic inverter and energy storage converter. The stability of off-grid operation is improved by using droop control or virtual synchronous machine technology. Off-grid power dispatch strategy: When the microgrid output is insufficient, the DMS performs tiered power supply according to load priority, prioritizes critical loads, optimizes the charging and discharging plan of energy storage equipment to avoid overcharging or over-discharging, and uses model predictive control or artificial intelligence algorithms to dynamically adjust power allocation.

[0016] Furthermore, the triggering conditions for "microgrid needs to perform grid-connected / off-grid mode switching" include distribution network faults that prevent the microgrid from disconnecting from the grid on its own, distribution network maintenance requiring the microgrid to disconnect from operation, and the microgrid needing to be restored to grid connection after fault handling or maintenance is completed. When the mode is switched from off-grid to grid-connected, the switching control command issued by the DMS to the grid-connected switch must meet the preset synchronization conditions. The synchronization conditions include that the voltage difference between the two sides is within the preset safety range, the frequency difference is within the preset synchronization range, the phase difference is within the preset deviation range, and the phase sequence on both sides is consistent. During the process of switching off-grid or on-grid, the microgrid control system sends operation signals to the DMS through the converged terminal, and the DMS manages and displays the status of the switching process.

[0017] Furthermore, the black boot recovery process coordinated by the main station includes: The DMS-controlled energy storage device establishes a microgrid voltage and frequency reference in a voltage and frequency control mode, and simultaneously calculates the duration of the current energy storage output. The DMS sends power access commands to the microgrid control system in a preset order to realize the orderly grid connection of distributed power sources. The preset order includes at least the access priority of photovoltaic and wind power. The DMS first issues critical load restoration instructions, and after the microgrid is running stably, it issues general load gradually access instructions; during the process, non-critical load removal instructions are generated to ensure microgrid power balance.

[0018] Furthermore, the DMS's evaluation of the command execution effect includes voltage compliance rate, frequency deviation, active power balance deviation rate, reactive power balance deviation rate, and average outage duration of critical loads. Among these requirements, the voltage qualification rate must meet the preset qualification standard, calculated as the ratio of the duration of voltage within the allowable range to the total operating time of the microgrid multiplied by 100%; the frequency deviation must be controlled within the preset stable range; the active power balance deviation rate and the reactive power balance deviation rate must both meet the preset deviation standards, calculated as the ratio of (total active power output of the microgrid - total active power load) to the total active power load multiplied by 100%, and the ratio of (total reactive power output of the microgrid - total reactive power demand) to the total reactive power demand multiplied by 100%; and the average outage duration of critical loads must meet the preset recovery standard.

[0019] Furthermore, the instructions issued by the DMS are transmitted to the execution devices within the microgrid via a communication network, including at least a photovoltaic inverter, an energy storage converter, and a load controller. Among them, the instructions sent to the photovoltaic inverter are output adjustment instructions, the instructions sent to the energy storage converter are charge / discharge mode switching instructions, and the instructions sent to the load controller are load tiered switching instructions.

[0020] Furthermore, before issuing instructions, the DMS predicts the future operating status of the microgrid based on big data analysis and machine learning algorithms to ensure that the instructions match the actual needs of the microgrid.

[0021] And, a low-voltage distribution area microgrid dispatch and control system based on a distribution master station, comprising: The Distribution Automation Master Station (DMS) is configured as follows: It establishes a low-voltage distribution area microgrid model, collects operational data within the microgrid, and monitors the microgrid's operational status in real time; when it detects that the microgrid needs to operate off-grid or is already off-grid, it formulates an optimized scheduling strategy to maintain off-grid stability based on the operational data; when it detects that the microgrid needs to perform a grid-connected / off-grid mode switchover, it first issues a mode switchover preparation command, and after the source-load-storage equipment within the microgrid has completed pre-adjustment, it issues a switching control command to the grid-connected switch; when it detects that the microgrid is disconnected from the distribution network and is in a complete shutdown state, it initiates a black-start recovery process coordinated by the master station and issues a black-start command containing the recovery sequence; it evaluates the execution effect of various commands, and if the preset requirements are not met, it dynamically adjusts and optimizes the scheduling strategy. The microgrid control system is connected to the DMS and is configured to receive scheduling strategies, mode switching preparation instructions and black start instructions issued by the DMS, and to perform pre-adjustment and orderly control of the source load storage equipment in the microgrid. The grid connection switch is connected to the DMS and configured to receive the switching control command issued by the DMS and perform the microgrid grid connection or off-grid mode switching operation. The execution device is communicatively connected to the DMS or microgrid control system and is configured to receive scheduling instructions and execute corresponding output adjustment, charging / discharging mode switching or load switching operations.

[0022] And an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0023] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0024] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects: This invention uses the Distribution Automation Master Station System (DMS) as the global centralized control hub, replacing the traditional microgrid's decentralized decision-making mode that relies on local controllers. It effectively solves the problem of the lack of global optimization capabilities in existing technologies. By integrating multi-source operation data within the microgrid and coordinating the formulation and execution of scheduling strategies through the DMS, it can balance source, load, and storage resources from the overall perspective of the distribution network, avoid operational deviations caused by local decisions, and significantly improve the stability and economy of low-voltage distribution area microgrid operation.

[0025] In the off-grid operation scenario of microgrids, this invention achieves coordinated optimization of off-grid frequency and voltage scheduling strategies and off-grid power scheduling strategies. It can maintain the stability of system frequency and voltage in the off-grid state by adjusting the output of photovoltaic inverters and energy storage converters and by using droop control or virtual synchronous machine technology. It can also achieve graded power supply according to load priority, giving priority to ensuring the continuous power supply of critical loads. At the same time, it optimizes the charging and discharging plan of energy storage equipment to avoid overcharging and over-discharging, thus taking into account the reliability of off-grid operation and the service life of equipment.

[0026] To address the issue of transient power outages or power surges that can easily occur during grid-connected / off-grid switching in existing technologies, this invention employs an orderly process of "DMS pre-issuing preparation instructions - microgrid control system adjusting source, load, and storage equipment - then issuing switching control instructions," combined with synchronization control during off-grid to grid-connected transitions. This ensures that the voltage, frequency, phase, and other parameters of the microgrid and the distribution network match during the switching process, significantly reducing operational risks and achieving safe and smooth mode switching.

[0027] In the scenario of complete microgrid outage recovery, this invention relies on the master station collaborative black start recovery process led by DMS, with energy storage devices taking the lead in establishing voltage and frequency references, guiding distributed power sources to connect to the grid in an orderly manner and restoring load power supply in stages according to a preset sequence. This solves the problem of slow black start recovery speed caused by the lack of master station collaboration in existing solutions, and can quickly rebuild the microgrid's autonomous power supply capability, shorten the outage time of critical loads, and improve the microgrid's resilience to faults.

[0028] Furthermore, this invention utilizes a closed-loop evaluation and dynamic optimization of command execution effects through DMS, combined with big data analysis and machine learning algorithms to predict the future operating status of the microgrid. This allows for continuous adjustment of the scheduling strategy based on actual operational feedback, making the scheduling commands more adaptable to the real-time operating conditions and changing trends of the microgrid, thus avoiding the problem of insufficient adaptability of traditional fixed strategies. Simultaneously, it provides a matching scheduling control system and computer equipment to implement this method, providing hardware support for its practical application and further enhancing the feasibility and practical value of the solution. This comprehensively improves the shortcomings of existing microgrid scheduling technologies in terms of reliability, economy, and adaptability. Attached Figure Description

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Figure 1 This is a flowchart illustrating the overall process of an embodiment of the present invention. Detailed Implementation

[0030] In the following, specific embodiments of this application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand and implement this application. Without departing from the principles of this application, features from various embodiments can be combined to obtain new implementations, or certain features from some embodiments can be substituted to obtain other preferred implementations.

[0031] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in conjunction with the accompanying drawings: The purpose of this invention is to provide a low-voltage distribution area microgrid dispatch and control method based on a distribution master station. First, a microgrid model is established in the DMS system, and various operational data, such as power supply side data, load side data, and energy storage device data, are collected within the low-voltage distribution area microgrid to monitor its grid-connected operation status in real time. Second, based on data analysis and combined with the actual operating conditions of the low-voltage distribution area microgrid, off-grid frequency and voltage dispatch and off-grid power dispatch strategies are formulated and optimized to achieve grid-connected / off-grid balanced switching. Finally, black-start recovery is initiated according to actual needs.

[0032] This invention enables real-time monitoring and intelligent scheduling of the low-voltage distribution area microgrid's operational status through the distribution master station, achieving optimized microgrid operation and efficient energy utilization. The method includes steps such as DMS modeling and data acquisition, real-time monitoring of microgrid operation, formulation and dynamic optimization of scheduling strategies, smooth grid-connected / off-grid switching, and black-start recovery. It solves the problem of multi-source coordinated control in microgrids, effectively improving the power supply reliability and economy of low-voltage distribution area microgrids.

[0033] like Figure 1 As shown, this embodiment of the invention provides a low-voltage distribution area microgrid dispatching and control method based on a distribution master station, comprising the following steps: Step S1: Establish a low-voltage distribution area microgrid model based on the distribution automation master station system (DMS), and collect various operational data such as power supply side data, load side data, and energy storage equipment data within the low-voltage distribution area microgrid.

[0034] Step S2: Based on step 1, DMS interacts with the microgrid control system through the fusion terminal to monitor the microgrid's operating status in real time.

[0035] Step S3: Based on step 2, and based on real-time data analysis, DMS, in conjunction with the actual operating status of the low-voltage distribution area microgrid, formulates and optimizes off-grid frequency and voltage scheduling and off-grid power scheduling strategies.

[0036] Step S4: Based on the needs of maintenance or emergency repair, the DMS generates instructions for switching the low-voltage microgrid from grid connection to off-grid and from off-grid to grid connection and sends them to the microgrid system.

[0037] Step S5: After the low-voltage microgrid is disconnected from the distribution network, the DMS enters the black start recovery process when it detects that all transformer substations in the microgrid are out of power.

[0038] Step S6: The DMS system converts relevant scheduling strategies and grid connection / offline operations into specific scheduling instructions and sends them to the execution devices in the low-voltage distribution area microgrid through the communication network. The execution devices adjust and control according to the scheduling instructions.

[0039] Step S7: Evaluate and provide feedback on the scheduling effect based on the execution status of the instructions. If the scheduling effect is unsatisfactory, adjust and optimize the strategy based on the feedback information to achieve better scheduling results.

[0040] As a preferred embodiment, in step S1, the DMS establishes a microgrid model including various power sources, loads, energy storage devices, etc., based on the actual situation of the low-voltage distribution area microgrid. Through various sensors and measuring devices installed within the low-voltage distribution area microgrid, operational data such as power source data, load data, and energy storage device data are collected in real time.

[0041] in, Power supply side data: including photovoltaic power generation, wind power generation, diesel generator status, etc.; Load-side data: including power load of each low-voltage distribution area, priority of critical loads, etc. Energy storage device data includes battery SOC, charging and discharging power, and energy storage capacity. Power grid operation data: including distribution network voltage, frequency, power flow, etc.

[0042] In a preferred embodiment, in step S2, the DMS interacts with the microgrid control system via a converged terminal to monitor the microgrid's operating status in real time, including: voltage, frequency, and power balance during grid-connected operation; the matching of distributed power output with load demand; and the charging / discharging status and remaining capacity of energy storage devices. If an anomaly is detected (such as voltage exceeding limits or frequency fluctuations), the DMS immediately activates an early warning mechanism to provide a basis for subsequent scheduling strategy optimization.

[0043] As a preferred embodiment, in step S3, the DMS collects and monitors the frequency and voltage of the low-voltage AC side of the distribution area. When the low-voltage microgrid is off-grid, if the frequency of the low-voltage AC side of the distribution area is lower than the safety threshold, the DMS system automatically generates an alarm. Off-grid frequency and voltage scheduling strategies are formulated and optimized: When the microgrid is off-grid, the output of distributed power sources (such as photovoltaic inverters and energy storage converters) is adjusted to maintain system frequency and voltage stability; droop control or virtual synchronous machine technology is used to improve off-grid operation stability.

[0044] As a preferred embodiment, in step S3, the DMS system monitors the microgrid output in real time. When the low-voltage microgrid output is insufficient, it formulates an off-grid power scheduling strategy, including: Power supply is tiered according to load priority, with priority given to critical loads (such as hospitals and communication base stations). Optimize energy storage charging and discharging strategies to avoid overcharging / over-discharging and extend battery life; Dynamic optimization is achieved by employing model predictive control or artificial intelligence algorithms. However, the proposed predictive model or artificial intelligence algorithm is not the focus of this solution design, and both are currently mature technologies in the field. Those skilled in the art can choose appropriate implementation methods within the scope of existing technologies under the framework of this invention.

[0045] As a preferred embodiment, in step S4, when the low-voltage distribution area microgrid cannot autonomously disconnect from the grid after a fault occurs, or when the microgrid needs to disconnect during distribution network maintenance, the DMS system will issue a grid-connection to disconnection control command to the microgrid. The microgrid controller, based on the master station command, regulates the source-load-storage equipment within the microgrid system and then notifies the remote distribution network master station to remotely trip the grid-connection switch, completing the disconnection operation. During the disconnection process, the fusion terminal sends relevant operation signals to the DMS, which then receives and manages the grid-connection to disconnection process.

[0046] As a preferred embodiment, in step S4, after fault handling or maintenance is completed and the microgrid needs to be connected to the grid, the DMS notifies the microgrid controller to prepare for the grid connection operation and then issues a synchronization check remote control closing operation to the microgrid's grid connection switch. For example, through the DMS monitoring interface, the "synchronization check closing" mode can be selected for the switch that needs to be closed, and the synchronization-related parameters can be set. The closing command is then sent to the microgrid system. This operation can also be set to be executed automatically. After the switch is successfully remotely controlled, the microgrid controller adjusts the microgrid source-load-storage equipment according to the grid connection success signal to complete the off-grid to grid connection transition. During the grid connection process, the fusion terminal sends relevant operation signals to the DMS, and the DMS system performs process control and display of the off-grid to grid connection transition.

[0047] Among these, the following four conditions must be met before energization can be allowed: Voltage conditions: Measure the effective value of the voltage on both sides and calculate the difference. The voltage difference should be <5%~10% of the rated voltage. Frequency requirements: The frequency is accurately measured using zero-crossing detection or phase-locked loop (PLL) technology, with a frequency difference of <0.1~0.25Hz; Phase condition: Compare the phase difference between the voltages on both sides; the phase difference < 10º. Phase sequence condition: Both sides must be identical.

[0048] As a preferred embodiment, in step S5, when the low-voltage distribution microgrid is disconnected from the distribution network, the DMS system initiates a black-start recovery process when it detects a power outage in all distribution areas within the microgrid: Energy storage is prioritized for startup: energy storage devices are the first to establish the microgrid voltage and frequency benchmark; Distributed power sources are gradually integrated: photovoltaic, wind power, and other power sources are restored in a preset order; Load tiered recovery: Prioritize the recovery of critical loads, and then gradually restore general loads.

[0049] As a preferred embodiment, in step S6, the DMS system converts optimized scheduling strategies and grid connection / off-grid operations into specific control commands. Through the communication network, it remotely controls and regulates microgrid execution devices such as photovoltaic inverters (adjusting output), energy storage converters (switching charging and discharging modes), and load controllers (performing tiered switching), thereby realizing intelligent scheduling and control of the low-voltage microgrid.

[0050] The dispatching strategies include power supply-side dispatching, load-side dispatching, and energy storage device dispatching. In terms of power supply-side dispatching, the output allocation of distributed power sources is optimized based on the power generation forecasts of the low-voltage microgrid and the load demand of the distribution network. In terms of load-side dispatching, peak shaving and valley filling, as well as energy conservation and consumption reduction, are achieved through refined load management and the implementation of demand-side response strategies. In terms of energy storage device dispatching, reasonable charging and discharging plans are formulated based on the energy demand of the microgrid and the charging and discharging characteristics of the energy storage devices to improve the utilization rate and economic benefits of the energy storage devices.

[0051] In a preferred embodiment, in step S7, the DMS evaluates the execution effect of the scheduling command, including: whether the voltage and frequency are stable; whether the power balance meets the standards; and whether the power supply to critical loads is reliable. If the evaluation results do not meet the requirements, the DMS adjusts the strategy based on the feedback information to form a closed-loop optimization.

[0052] The specific evaluation criteria can be found in the table below:

[0053] Compared with the prior art, the advantages of the solutions provided in the embodiments of the present invention include: 1. Global Optimized Scheduling: Through centralized decision-making by DMS, the economy and stability of low-voltage distribution area microgrid operation are improved; 2. Fast mode switching: Millisecond-level command issuance ensures seamless transition between grid connection and off-grid operation; 3. Intelligent Black Start: Master station collaborative control shortens microgrid fault recovery time; 4. Adaptive adjustment: The closed-loop feedback mechanism enables dynamic optimization of strategies, ensuring the stable operation of low-voltage distribution area microgrids and the reliability of power supply, thereby improving the overall performance of the power system and user satisfaction.

[0054] Based on the above design of this invention, and combined with a photovoltaic-energy storage microgrid example, the testing and implementation process is as follows: Data Acquisition: The DMS master station establishes a microgrid model including various power sources, loads, and energy storage devices. The DMS collects photovoltaic power generation, load demand, and energy storage SOC to predict the operating status for the next 2 hours.

[0055] Real-time monitoring: When a voltage drop in the distribution network is detected, the DMS activates the off-grid switching contingency plan.

[0056] Off-grid dispatch: Energy storage switches to VSG mode to maintain voltage and frequency stability.

[0057] Black start: If the low-voltage distribution area microgrid loses power completely, power supply will be restored in the order of "energy storage → photovoltaic → load".

[0058] Effect evaluation: After grid connection, the energy storage charging and discharging plan is optimized to reduce operating costs.

[0059] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0060] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0061] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0062] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

[0063] This invention is not limited to the above-described preferred embodiments. Anyone inspired by this invention can derive other forms of low-voltage distribution area microgrid dispatch and control methods based on distribution master stations. All equivalent changes and modifications made within the scope of the claims of this invention shall fall within the scope of this invention.

Claims

1. A low-voltage transformer area microgrid dispatch control method based on a power distribution master station, characterized in that, A distribution automation master station system DMS is used to perform global centralized control, including: A low-voltage area microgrid model is established based on the DMS, operation data in the microgrid are collected, and the operation state of the microgrid is monitored in real time; When it is monitored that the microgrid needs to operate off-grid or is in an off-grid state, the DMS formulates and issues an optimal scheduling strategy for maintaining off-grid stability to a microgrid control system based on the operation data; When it is monitored that the microgrid needs to perform grid-connected / off-grid mode switching, the DMS first issues a mode switching preparation instruction to the microgrid control system, and then issues a switching control instruction to a grid-connected switch after the microgrid control system completes the pre-adjustment of source-load-storage devices in the microgrid, thereby leading the completion of mode switching; When it is detected that the microgrid is disconnected from the distribution grid and is in a full-stop state, the DMS starts a black start recovery process of the master station, issues a black start instruction containing a recovery sequence to the microgrid control system, and leads the step-by-step recovery of power supply from the energy storage device. The DMS evaluates the execution effect of the above instructions, and if the preset requirements are not met, dynamically adjusts the optimal scheduling strategy to form a closed-loop scheduling.

2. The low-voltage area microgrid scheduling control method based on a distribution master station according to claim 1, characterized in that: The operation data in the microgrid include power supply side data, load side data, energy storage device data and grid operation data; wherein the power supply side data includes photovoltaic power generation power, wind power generation power and diesel generator state, the load side data includes power consumption load of each low-voltage area and priority of key load, the energy storage device data includes battery state of charge, charging and discharging power and energy storage capacity, and the grid operation data includes distribution grid voltage, frequency and power flow direction; The real-time monitored operation state of the microgrid includes voltage stability, frequency fluctuation, power balance state, matching degree of distributed power output and load demand, and charging and discharging state and remaining capacity of the energy storage device when connected to the grid; if voltage out-of-limit or frequency fluctuation is abnormal, the DMS starts a warning mechanism.

3. The low-voltage area microgrid scheduling control method based on a distribution master station according to claim 1, characterized in that: The optimal scheduling strategy for maintaining off-grid stability includes off-grid frequency and voltage scheduling strategy and off-grid power scheduling strategy: The off-grid frequency and voltage scheduling strategy: the DMS collects the frequency and voltage of the low-voltage alternating current side of the area, automatically generates an alarm when the frequency is lower than the safety threshold, maintains the stability of the system frequency and voltage by adjusting the output of the photovoltaic inverter and the energy storage converter, and uses droop control or virtual synchronous machine technology to improve the off-grid operation stability; The off-grid power scheduling strategy: when the microgrid output is insufficient, the DMS performs hierarchical power supply according to the load priority, preferentially guarantees the key load, optimizes the charging and discharging plan of the energy storage device to avoid overcharging or overdischarging, and dynamically adjusts the power distribution by using model predictive control or artificial intelligence algorithm.

4. The low-voltage area microgrid scheduling control method based on a distribution master station according to claim 1, characterized in that: The trigger condition of the micro-grid needs to perform grid-connected / off-grid mode switching includes that the micro-grid cannot be autonomously disconnected from the grid due to power distribution network failure, the micro-grid needs to be disconnected from operation due to power distribution network maintenance, and the micro-grid needs to restore grid connection after fault handling or maintenance is completed; When the mode switching is off-grid to grid connection, the switching control instruction issued by the DMS to the grid-connected switch needs to meet preset synchronization conditions, and the synchronization conditions include that the voltage difference on both sides is in a preset safe range, the frequency difference is in a preset synchronization range, the phase difference is in a preset deviation range, and the phase sequence on both sides is consistent; During the off-grid or grid connection switching process, the micro-grid control system uploads operation signals to the DMS through a fusion terminal, and the DMS controls and displays the state of the switching process.

5. The low-voltage micro-grid dispatching control method based on the power distribution master station according to claim 1, characterized in that: The black start recovery process coordinated by the master station includes: The DMS controls the energy storage device to establish a micro-grid voltage and frequency reference in a voltage and frequency control mode, and simultaneously calculates the current energy storage output duration; The DMS issues power source access instructions to the micro-grid control system in a preset order to realize orderly grid connection of distributed power sources, and the preset order at least includes the access priority of photovoltaic and wind power; The DMS first issues critical load recovery instructions, and then issues general load access instructions step by step after the micro-grid is stable; during the process, non-important load shedding instructions are generated to ensure micro-grid power balance.

6. The low-voltage micro-grid dispatching control method based on the power distribution master station according to claim 1, characterized in that: The DMS evaluates the effect of the instructions, including voltage qualification rate, frequency deviation, active power balance deviation rate, reactive power balance deviation rate, and average outage duration of critical load; The voltage qualification rate needs to meet a preset qualification standard, and the calculation method is to multiply the ratio of the time when the voltage is in the allowed range to the total micro-grid operation time by 100%; the frequency deviation needs to be controlled within a preset stable interval; the active power balance deviation rate and the reactive power balance deviation rate both need to meet preset deviation standards, and the calculation methods are to multiply the ratio of the total active output of the micro-grid to the total active load by 100%, and to multiply the ratio of the total reactive output of the micro-grid to the total reactive demand by 100% respectively; the average outage duration of critical load needs to meet a preset recovery standard.

7. The low-voltage micro-grid dispatching control method based on the power distribution master station according to claim 1, characterized in that: The instructions issued by the DMS are transmitted to the execution devices in the micro-grid through a communication network, including photovoltaic inverters, energy storage converters, and load controllers; The instructions issued to the photovoltaic inverters are output adjustment instructions, the instructions issued to the energy storage converters are charge and discharge mode switching instructions, and the instructions issued to the load controllers are load hierarchical switching instructions.

8. The low-voltage area micro-network dispatching control method based on a power distribution master station according to claim 7, characterized in that: Before formulating the instructions, the DMS predicts the future operation state of the micro-grid based on big data analysis and machine learning algorithms to ensure that the instructions match the actual needs of the micro-grid.

9. A low-voltage transformer area microgrid dispatch control system based on a power distribution master station, characterized in that, It includes: The power distribution automation master station system DMS is configured to: establish a low-voltage micro-grid model, collect operation data in the micro-grid, and monitor the operation state of the micro-grid in real time; When it is monitored that the microgrid needs to operate off-grid or is in off-grid state, an optimal scheduling strategy for maintaining off-grid stability is formulated based on the operation data; when it is monitored that the microgrid needs to perform grid-connected / off-grid mode switching, mode switching preparation instructions are first issued, and then switching control instructions are issued to the grid-connected switch after the pre-adjustment of the source-load-storage devices in the microgrid is completed; when it is detected that the microgrid is disconnected from the distribution network and is in a full-stop state, a black start recovery process with the cooperation of the master station is started and black start instructions containing a recovery sequence are issued; the execution effect of various instructions is evaluated, and if the preset requirements are not met, the optimal scheduling strategy is dynamically adjusted; The microgrid control system is in communication connection with the DMS and is configured to receive the scheduling strategy, mode switching preparation instructions and black start instructions issued by the DMS, and to perform the pre-adjustment and orderly control of the source-load-storage devices in the microgrid; The grid-connected switch is in communication connection with the DMS and is configured to receive the switching control instructions issued by the DMS and to perform the mode switching operation of the microgrid grid-connected or off-grid; The execution device is in communication connection with the DMS or the microgrid control system and is configured to receive the scheduling instructions and to perform the corresponding output adjustment, charge-discharge mode switching or load switching operation.

10. A computer device, comprising: The computer program product comprises a processor and a memory storing the computer program, and the processor implements the method of any one of claims 1-8 when executing the computer program.

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