Distributed optical storage autonomous alternating control method for low-voltage distribution network based on multi-objective optimization

Abstract

The invention discloses a low-voltage distribution network distributed optical storage autonomous alternating control method based on multi-objective optimization, and the method comprises the following steps: collecting dynamic data of a power grid in real time, and sharing the dynamic data through a distributed communication network; a distributed intelligent agent is configured for each photovoltaic unit and each energy storage device; constructing a multi-objective optimization algorithm taking voltage stability as a core, and generating a photovoltaic and energy storage optimal cooperation strategy in real time; the photovoltaic unit dynamically adjusts the output power according to the optimal cooperation strategy and the current power grid state; the energy storage system autonomously switches charging and discharging modes based on the output fluctuation condition of a photovoltaic unit and the dynamic state of a power grid in combination with an optimal cooperation strategy; an alternate start-stop mechanism is introduced between the photovoltaic units; the state of each device is synchronized in real time through multi-point communication; and dynamically adjusting and optimizing algorithm parameters according to the real-time operation data. Stability and reliability of a power grid can be improved, service life of equipment can be prolonged, and cost can be reduced.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation and grid voltage management technology, and in particular to a distributed photovoltaic-storage autonomous alternation control method for low-voltage distribution networks based on multi-objective optimization. Background Technology

[0002] With the rapid development of renewable energy, the integration of distributed photovoltaic (PV) systems into low-voltage distribution networks is increasing, bringing significant energy-saving and emission-reduction benefits to the power grid. However, the random and intermittent power generation characteristics of PV systems pose numerous challenges to the stable operation of the power grid. Traditional PV control methods often focus on optimizing a single objective, such as maximizing PV utilization or minimizing line losses, without prioritizing voltage stability and neglecting the coordinated management of voltage deviation, overshoot, and other issues with energy storage efficiency and line losses. This leads to frequent voltage fluctuations and overshoot caused by the randomness of PV output, exceeding the ±5% allowable voltage deviation range and severely reducing the power supply quality and operational reliability of the power grid.

[0003] Furthermore, because the power generation characteristics of photovoltaic (PV) units are greatly affected by environmental factors such as sunlight and temperature, individual PV units may experience frequent start-ups and shutdowns when sunlight conditions change. This frequent start-up and shutdown not only accelerates the aging of the PV units and related equipment but also increases grid maintenance costs and reduces the overall economic efficiency of the system.

[0004] Therefore, in order to overcome the shortcomings of the prior art, this invention proposes a distributed photovoltaic-storage autonomous alternation control method for low-voltage distribution networks based on multi-objective optimization. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for autonomous alternation control of distributed photovoltaic and energy storage in low-voltage distribution networks based on multi-objective optimization, comprising the following steps:

[0007] The system collects dynamic data from the power grid in real time through sensors built into the photovoltaic units and energy storage system, and shares this data through a distributed communication network.

[0008] Each photovoltaic unit and energy storage device is equipped with a distributed intelligent agent to enable it to autonomously judge the grid status and execute dynamic adjustment commands.

[0009] A multi-objective optimization algorithm with voltage stability as the core is constructed. Priority is given to ensuring that the grid voltage deviation is controlled within the set range. Then, the optimal synergistic strategy of photovoltaic and energy storage is generated in real time by comprehensively considering energy storage efficiency, line loss and photovoltaic utilization rate.

[0010] The photovoltaic unit dynamically adjusts its output power based on the optimal coordination strategy and the current grid status, and feeds back the adjusted output to the energy storage system in real time to assist the energy storage system in optimizing its power compensation strategy.

[0011] Based on the output fluctuations of photovoltaic units and the dynamic state of the power grid, the energy storage system autonomously switches charging and discharging modes in conjunction with the optimal coordination strategy, provides real-time power compensation to the power grid, and dynamically updates feedback data for photovoltaic units to adjust, forming a closed-loop optimization adjustment mechanism.

[0012] An alternating start-stop mechanism is introduced among photovoltaic units, combined with the regulation of the energy storage system, to reduce the frequent start-stop of individual photovoltaic units and equipment aging;

[0013] The status of each device and grid voltage data are synchronized in real time through multi-point communication. When an abnormal voltage is detected, the energy storage system is activated first to stabilize the voltage in an emergency, and then other photovoltaic units are notified to make coordinated adjustments.

[0014] The algorithm parameters are dynamically adjusted and optimized based on real-time operating data to continuously improve the system's stability and photovoltaic utilization efficiency.

[0015] Compared with the prior art, the significant advantages of this invention are:

[0016] 1. This invention identifies core indicators for multi-objective optimization, prioritizing voltage stability while considering power quality, energy storage efficiency, photovoltaic utilization, and line losses as other core indicators. A dynamic weight adjustment mechanism is introduced to rapidly respond to changes in grid conditions and generate real-time optimal collaborative strategies. The photovoltaic units and energy storage system form a closed-loop adjustment mechanism, coordinating power output and compensation strategies. This significantly reduces grid voltage overshoot and frequency deviation, thereby improving grid stability and reliability. In practical applications, grid voltage deviation can be strictly controlled within ±5%, and voltage fluctuation amplitude is reduced by more than 30% compared to traditional methods, resulting in a significant improvement in power supply quality.

[0017] 2. This invention introduces an alternating start-stop mechanism between photovoltaic units, combined with the intelligent adjustment of the energy storage system, to avoid frequent start-stop of individual photovoltaic units and slow down equipment aging. Simultaneously, the energy storage system autonomously switches charging and discharging modes based on photovoltaic output fluctuations to achieve power balance and fluctuation suppression, reducing system operating load, thereby extending equipment lifespan and reducing maintenance and operating costs. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0019] Figure 1 This is a flowchart of a distributed photovoltaic-storage autonomous alternation control method for low-voltage distribution networks based on multi-objective optimization, according to the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0024] Reference Figure 1 As an embodiment of the present invention, a distributed photovoltaic-storage autonomous alternation control method for low-voltage distribution networks based on multi-objective optimization is provided, comprising the following steps:

[0025] By utilizing sensors built into the photovoltaic units and energy storage system, dynamic data of the power grid is collected in real time. The key data collected includes voltage (instantaneous value, deviation, and fluctuation amplitude), frequency, load fluctuations, photovoltaic output, and the charging and discharging status of the energy storage system. This data is shared through a distributed communication network, providing precise data support for voltage control. Real-time data acquisition and sharing ensure that all devices have a comprehensive understanding of the power grid's status, thereby improving the system's response speed. It also provides accurate and real-time input data for subsequent optimization decisions, ensuring system stability.

[0026] Each photovoltaic unit and energy storage device is equipped with a distributed intelligent agent, enabling it to autonomously assess grid conditions and execute dynamic adjustment commands. This autonomous decision-making function reduces reliance on a central control system, enhancing the system's autonomy and flexibility. It allows the photovoltaic units and energy storage system to respond instantly to local grid conditions, ensuring grid stability.

[0027] Specifically, configuring a distributed intelligent agent includes the following steps:

[0028] Assign a unique device identifier to each photovoltaic unit and energy storage system, and configure the necessary communication interfaces (such as Wi-Fi, LoRa, 5G, etc.) to support distributed communication.

[0029] Install sensor modules that include real-time acquisition functions for voltage, frequency, power, etc., and bind them with intelligent agents to achieve real-time perception of device status.

[0030] Configure an embedded processor or microcontroller (such as an ARM chip), load a local data preprocessing algorithm, and realize real-time analysis and preliminary judgment of status data;

[0031] A data sharing network between photovoltaic units and energy storage devices is established using distributed communication protocols (such as MQTT, ZigBee, etc.) to support bidirectional transmission of status data and control commands;

[0032] Load a distributed collaborative decision-making algorithm into each smart agent, enabling it to generate independent adjustment instructions based on local state and shared information;

[0033] Synchronous testing of the proxy functions of each device was conducted to ensure state sharing, stable communication, and consistency in the execution of collaborative decisions.

[0034] Configure the operating permissions for the intelligent agent. For example, whether it is allowed to execute global scheduling commands with priority, and whether it can independently start and stop photovoltaic units or energy storage devices.

[0035] Furthermore, distributed intelligent agents include:

[0036] The status sensing module focuses on collecting instantaneous voltage values, deviations, and fluctuation amplitudes, as well as sensors for frequency, power, and temperature, to collect real-time data on the operating status of the power grid and equipment.

[0037] The data preprocessing and judgment unit integrates edge computing capabilities and can filter, smooth, and make preliminary judgments on the raw data collected by the sensor (such as excessive voltage or frequency deviation).

[0038] The collaborative optimization algorithm unit includes a distributed optimization algorithm, which can generate real-time adjustment strategies based on local sensing data and shared neighborhood states (local sensing data refers to the operating status of the power grid and equipment collected by each photovoltaic and energy storage device through sensors, and shared neighborhood states refer to the operating status of the power grid and equipment transmitted by other photovoltaic and energy storage devices through the communication module).

[0039] The communication module supports low-power, low-latency data transmission protocols, such as MQTT, LoRa, or 5G networks, for sharing status data and transmitting control commands between devices.

[0040] The execution unit is an execution module that drives the adjustment of the output power of the photovoltaic unit, the switching of charging and discharging of energy storage, and the start-up and shutdown of the equipment.

[0041] The fault detection and emergency response unit integrates machine learning capabilities, which can dynamically optimize the photovoltaic and energy storage collaborative control strategy based on historical operating data and grid status, thereby improving response capabilities.

[0042] The interface and update module reserves hardware and software interfaces to support remote access from external systems and online algorithm updates, thereby improving the scalability and maintainability of the agent.

[0043] A multi-objective optimization algorithm with voltage stability as its core is constructed, which integrates energy storage efficiency and line losses to generate the optimal synergistic strategy for photovoltaic and energy storage in real time. This step, by incorporating various grid performance indicators, ensures effective system optimization under different conditions, avoiding system instability caused by localized optimization.

[0044] Specifically, a multi-objective optimization algorithm with voltage stability as its core is constructed to generate the optimal synergistic strategy between photovoltaics and energy storage in real time, including the following steps:

[0045] The core indicators for multi-objective optimization are determined, including voltage stability, power quality, energy storage efficiency, photovoltaic utilization rate, and line loss.

[0046] Establish a mathematical model centered on the objective function, where the objective function integrates various optimization objectives in the form of weights, such as:

[0047]

[0048] in, Indicates voltage stability. Indicates energy storage efficiency. Indicates line loss. , , The weights can be dynamically adjusted.

[0049] Define constraints in the optimization process, such as the maximum output power constraint of photovoltaic units, the charging and discharging capacity limit of energy storage systems, the line load capacity limit, and the stability constraint that the grid voltage deviation must be controlled within ±5%.

[0050] Real-time data such as voltage and power are acquired from sensors in photovoltaic units and energy storage systems. The data is preprocessed using filtering and noise reduction techniques to ensure the accuracy of the optimized input data.

[0051] Based on the real-time status of the power grid (such as load fluctuations, changes in photovoltaic output, etc.), the target weights are dynamically adjusted and optimized through fuzzy logic control or adaptive algorithms. When the voltage is abnormal, the voltage stability weight is adjusted to the highest level to prioritize the voltage stability requirements.

[0052] The objective function is solved using a fast optimization algorithm (such as particle swarm optimization, genetic algorithm or gradient descent) to generate the optimal photovoltaic and energy storage coordinated control strategy.

[0053] The generated optimal strategy is decomposed into power adjustment commands for photovoltaic units and charging / discharging modes for energy storage systems, and then distributed to each distributed intelligent agent through a distributed communication network.

[0054] Real-time monitoring of the matching degree between optimization results and power grid status, collection of feedback data, and adjustment of objective function weights. , , This involves setting or optimizing algorithm parameters (typically including initial values, iteration count, convergence threshold, and particle swarm size) to continuously optimize the cooperative strategy in dynamic power grid environments. For example, increasing these parameters when voltage fluctuations are large. The weighting of voltage stability should be prioritized; when energy storage devices are frequently charged and discharged, the weighting should be increased. The weighting of energy storage can improve its efficiency.

[0055] In the above steps, intelligent optimization is achieved by dynamically adjusting the objective function weights, enabling adaptation to changing power grid environments. Furthermore, by utilizing sensor data and distributed communication, optimal collaborative strategies can be generated in real time, ensuring efficient and stable power grid operation.

[0056] The photovoltaic (PV) units dynamically adjust their output power based on the optimal coordination strategy and the current grid state, and feed back the adjusted output status to the energy storage system in real time. This assists the energy storage system in optimizing its power compensation strategy, thereby preventing voltage overshoot or frequency drift. In this step, the coordinated optimization between the PV units and the energy storage system effectively balances instantaneous grid fluctuations and prevents grid instability. Furthermore, the feedback mechanism enables closed-loop control, which dynamically adjusts the grid state, thereby improving grid reliability and energy efficiency.

[0057] Specifically, the power compensation formula is as follows:

[0058]

[0059] in:

[0060] Power compensation value of the energy storage system, in watts;

[0061] , The starting and ending points of the time integral, in seconds;

[0062] The number of distributed photovoltaic units or energy storage systems is a positive integer.

[0063] : No. Each unit at time The power fluctuation value is collected in real time by the sensor;

[0064] : No. The voltage difference of each unit during the fluctuation period;

[0065] Attenuation factor, reflecting the sensitivity of different units to power fluctuations;

[0066] : Dynamic fluctuation function, simulating the first Power output fluctuations of individual units;

[0067] : No. The power fluctuation angular frequency of each unit, in radians per second;

[0068] : No. The phase offset of each unit, in radians per second;

[0069] : Combining the dynamic characteristics of voltage and current, it describes the fluctuation state of the power grid;

[0070] The average voltage value of the system over a period of time, expressed in volts;

[0071] Rate of change of current, measured in amperes per second;

[0072] Time decay factor: This indicates that the impact of earlier data on the current compensation gradually decreases.

[0073] Weight decay factor for historical data;

[0074] This describes the contributions of photovoltaic units and energy storage devices to real-time power fluctuations.

[0075] Integrate the overall dynamic state of the power grid and consider the impact of time decay on real-time compensation;

[0076] Meaning of the range:

[0077] >0: The energy storage system output power is compensated;

[0078] =0: Power fluctuation is within the allowable range and no compensation is required;

[0079] <0: The energy storage system absorbs excess power and performs negative compensation.

[0080] The power compensation formula uses voltage deviation System average voltage Using the core input parameter, the energy storage compensation is accurately calculated by quantifying the coupling relationship between voltage fluctuations and power fluctuations, ensuring that the voltage quickly returns to the allowable range.

[0081] Specifically, the photovoltaic unit dynamically adjusts its output power based on the optimal coordination strategy and the current grid status, including the following steps:

[0082] Photovoltaic units use built-in sensors to collect data related to output power in real time, including voltage, current, temperature, radiation intensity, etc., while also receiving shared data from the grid or energy storage devices.

[0083] By utilizing distributed intelligent agents, adjustment needs are assessed in real time based on the current grid status (such as voltage fluctuations and frequency deviations) and the optimal coordination strategy. If the grid is in an overvoltage state, the strategy prioritizes reducing output power. If the grid voltage is too low, the output power is increased or energy storage compensation is requested as appropriate.

[0084] Based on the optimal coordination strategy and the operating status of the photovoltaic units, the specific adjustment value is calculated. The adjustment value is dynamically generated by the optimization algorithm, which integrates power grid demand and equipment characteristics.

[0085] The adjusted formula is as follows:

[0086]

[0087] in:

[0088] : Maximum power output of the photovoltaic unit;

[0089] : Dynamic coefficients determined in the optimization strategy;

[0090] : Response sensitivity adjustment coefficient;

[0091] Current voltage deviation, which is the difference between the voltage value at a certain measurement point and the target voltage value in actual power grid operation.

[0092] Adjust the output signal of the photovoltaic unit's power inverter, based on the calculated... Real-time control of power output to meet grid stability requirements;

[0093] If the deviation does not return to the normal range after the power grid status is monitored in real time, the next round of dynamic adjustment is triggered.

[0094] Based on the output fluctuations of the photovoltaic (PV) cells and the dynamic state of the power grid, the energy storage system autonomously switches between charging and discharging modes, combined with optimal coordination strategies, to provide real-time power compensation to the grid. This further balances grid fluctuations and dynamically updates feedback data for the PV cells to adjust, forming a closed-loop optimization mechanism. In this step, the energy storage system can automatically adjust its charging and discharging strategies, effectively suppressing grid frequency fluctuations and improving grid stability.

[0095] Introducing a voltage-state-based alternating start-stop mechanism among photovoltaic (PV) units, combined with the voltage regulation capabilities of the energy storage system, avoids voltage fluctuations caused by frequent start-stop cycles of individual PV units, while also reducing equipment aging. This step effectively extends the lifespan of PV equipment, reduces mechanical fatigue and performance degradation caused by frequent start-stop cycles, improves system operating efficiency, and lowers equipment maintenance costs.

[0096] Specifically, the alternating start-stop mechanism includes setting start-stop rules, prioritizing start-stop operations, adjusting dynamic strategies, coordinating scheduling mechanisms, handling exceptions, and recording and optimizing data.

[0097] The start / stop rules are set as follows:

[0098] Load balancing rule: Based on the current load and historical operating status of the photovoltaic units, prioritize shutting down photovoltaic units with lower loads and longer operating times.

[0099] Operating time rules: Set the maximum continuous operating time and minimum rest time for photovoltaic units to ensure that the equipment gets sufficient rest and extend its service life.

[0100] Energy storage status rules: Combine the charging and discharging status of the energy storage system to coordinate the start-up and shutdown times of photovoltaic units and avoid grid fluctuations caused by insufficient or overcharged energy storage.

[0101] The start / stop priority order is as follows:

[0102] The start-up and shutdown priority of each photovoltaic unit is dynamically adjusted based on the unit's historical operating data, equipment health status (such as temperature and aging level), and output efficiency.

[0103] In abnormal situations, prioritize enabling units in good health and disable units that are malfunctioning or experiencing performance degradation.

[0104] The dynamic strategy is adjusted as follows:

[0105] Real-time load monitoring: Monitors the real-time load demand of the power grid and dynamically adjusts the number of photovoltaic units that are started and stopped, so as to meet the power grid demand while avoiding resource waste.

[0106] Sunlight condition assessment: Based on sunlight intensity and weather forecast data, adjust the start-up and shutdown sequence of photovoltaic units, giving priority to units with high power generation efficiency.

[0107] The collaborative scheduling mechanism is as follows:

[0108] Synergy with energy storage systems: During alternating start-up and shutdown processes, the compensation capabilities of energy storage systems are combined to ensure the balance between power grid supply and demand.

[0109] Multi-point communication coordination: Through a distributed communication network, the status and start-up / shutdown plans of each photovoltaic unit are synchronized to avoid conflicts and duplicate start-ups / shutdowns.

[0110] The exception handling mechanism is as follows:

[0111] Emergency start / stop: When a fault is detected in a photovoltaic unit or energy storage system, other photovoltaic units are quickly started / stopped to compensate.

[0112] Health status assessment: Regularly check the operating status of each unit, and prioritize the shutdown of abnormal units for maintenance.

[0113] The data recording and optimization are as follows:

[0114] Record the time, grid status, and equipment performance data for each start-up and shutdown operation as a basis for subsequent optimization of the start-up and shutdown mechanism.

[0115] Based on historical data, optimize start-stop strategy parameters (such as maximum continuous operating time and minimum rest time) to adapt to dynamic grid demands.

[0116] The system synchronizes the status of each device in real time through multi-point communication. When an anomaly is detected, the energy storage system is activated first to stabilize the grid and other photovoltaic units are notified to adjust accordingly. This data synchronization and anomaly detection mechanism ensures that the system can take timely remedial measures when a fault occurs, avoiding larger-scale grid fluctuations.

[0117] The system dynamically adjusts and optimizes algorithm parameters based on real-time operational data, continuously improving system stability and photovoltaic utilization efficiency. Through dynamic adjustment, the system can better adapt to different grid conditions, ensuring optimal power output and energy utilization.

[0118] In summary, this method, by introducing distributed intelligent agents, multi-objective optimization algorithms, and alternating start-stop mechanisms, can maximize the utilization efficiency of photovoltaic and energy storage devices while ensuring grid stability, reducing the operational burden on equipment, extending equipment lifespan, and minimizing energy losses caused by grid fluctuations. Furthermore, it not only improves system stability but also enhances the system's adaptability and optimization capabilities, enabling it to meet operational needs under different grid conditions.

[0119] In summary, this invention constructs a multi-objective optimization algorithm with voltage stability as its core, comprehensively considering power quality, energy storage efficiency, photovoltaic utilization rate, and line loss as core indicators, and introduces a dynamic weight adjustment mechanism. This enables rapid response to changes in grid conditions and the generation of real-time optimal collaborative strategies. The photovoltaic units and energy storage system form a closed-loop adjustment mechanism, coordinating output power and compensation strategies, thereby significantly reducing grid voltage overshoot and frequency deviation, thus improving grid operational stability and reliability. This invention also avoids frequent start-stop cycles of individual photovoltaic units by introducing an alternating start-stop mechanism, combined with intelligent adjustment of the energy storage system, slowing down equipment aging. Simultaneously, the energy storage system autonomously switches charging and discharging modes based on photovoltaic output fluctuations to achieve power balance and fluctuation suppression, reducing system operating load, thereby extending equipment lifespan and reducing maintenance and operating costs.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A low-voltage distribution network distributed light storage autonomous alternating control method based on multi-objective optimization, characterized in that, The method comprises the following steps: Real-time collection of dynamic data of the power grid by sensors built in the photovoltaic unit and the energy storage system, and sharing of the data through a distributed communication network; Configuration of a distributed intelligent agent for each photovoltaic unit and energy storage device to realize autonomous judgment of the state of the power grid and execution of dynamic adjustment instructions; Construction of a multi-objective optimization algorithm with voltage stability as the core to preferentially ensure that the voltage deviation of the power grid is controlled within a set range, and then comprehensively consider the energy storage efficiency, line loss and photovoltaic utilization rate to generate an optimal collaborative strategy for the photovoltaic unit and the energy storage system in real time; Dynamic adjustment of the output power of the photovoltaic unit according to the optimal collaborative strategy and the current state of the power grid, and real-time feedback of the adjusted output to the energy storage system to assist the energy storage system in optimizing the power compensation strategy; Autonomous switching of the charging and discharging modes of the energy storage system based on the output fluctuation of the photovoltaic unit and the dynamic state of the power grid, combined with the optimal collaborative strategy, to provide real-time power compensation for the power grid and dynamically update feedback data for the photovoltaic unit to adjust the reference, forming a closed-loop optimization and adjustment mechanism; Introduction of an alternate start-stop mechanism among the photovoltaic units in combination with the regulation of the energy storage system to reduce the frequent start-stop and equipment aging of a single photovoltaic unit; Real-time synchronization of the state of each device and the voltage data of the power grid through multi-point communication, and preferential start of the energy storage system for voltage emergency stability when voltage anomaly is detected, and then notification of other photovoltaic units to collaboratively adjust; Dynamic adjustment of the parameters of the optimization algorithm according to real-time operation data to continuously improve the stability of the system and the utilization efficiency of the photovoltaic unit.

2. The low voltage distribution network distributed optical storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 1, characterized in that, The dynamic data includes voltage, frequency, load fluctuation, photovoltaic output and energy storage charging and discharging state.

3. The low voltage distribution network distributed photovoltaic storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 1, characterized in that, The configuration of a distributed intelligent agent for each photovoltaic unit and energy storage device comprises the following steps: Allocation of a unique device identifier to each photovoltaic unit and energy storage system, and configuration of a communication interface to support distributed communication; Installation of a sensor module with real-time collection function, and data binding with the intelligent agent to realize real-time sensing of the state of the device; Configuration of an embedded processor or microcontroller, loading of a local data preprocessing algorithm to realize real-time analysis and preliminary judgment of the state data; Use of a distributed communication protocol to establish a data sharing network to support bidirectional transmission of state data and control instructions; Loading of a distributed collaborative decision-making algorithm in each intelligent agent to enable the intelligent agent to generate independent adjustment instructions based on local state and shared information; Synchronous testing of the agent function of each device to ensure consistency of state sharing, stable communication and collaborative decision-making execution; Setting of the operation authority of the intelligent agent.

4. The low voltage distribution network distributed optical storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 1 or 2, characterized in that: The distributed intelligent agent comprises: A state sensing module for real-time collection of the operating state of the power grid and the device; A data preprocessing and judgment unit integrating edge computing capability to filter, smooth and preliminarily judge the original data collected by the sensor; A collaborative optimization algorithm unit containing a distributed optimization algorithm for generating real-time adjustment strategies based on local sensing data and shared neighborhood state; A communication module supporting a data transmission protocol for sharing of state data and transmission of control instructions among devices; An execution unit for driving the output power adjustment of the photovoltaic unit, the charging and discharging switching of the energy storage and the execution of the start-stop action of the device. Fault detection and emergency handling unit, integrated machine learning capabilities, dynamically optimize photovoltaic and energy storage collaborative control strategy according to historical operation data and grid state, improve response ability; Interface and update module, reserve hardware and software interface, support remote access of external system and online update of algorithm.

5. The low voltage distribution network distributed PV and storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 1, characterized in that: Build a multi-objective optimization algorithm with voltage stability as the core, and generate the optimal collaborative strategy of photovoltaic and energy storage in real time, including the following steps: Determine the core indicators of multi-objective optimization, with voltage stability as the primary target, supplemented by power quality, energy storage efficiency, photovoltaic utilization rate, and line loss; Establish a mathematical model with the target function as the core, where the target function integrates all optimization objectives in the form of weights; Define the constraints in the optimization process; Real-time data acquisition from photovoltaic units and energy storage system sensors, data preprocessing through filtering and denoising techniques; According to the real-time voltage state of the grid, dynamically adjust the optimization target weight through fuzzy logic control or adaptive algorithm, and adjust the voltage stability weight to the highest when the voltage is abnormal, to meet the voltage stability demand first; Use fast optimization algorithm to solve the target function and generate the optimal photovoltaic and energy storage collaborative control strategy; Decompose the generated optimal strategy into photovoltaic unit power adjustment instructions and energy storage system charging and discharging modes, and distribute them to each distributed intelligent agent through a distributed communication network; Real-time monitoring of the matching degree of optimization results and grid state, collecting feedback data, adjusting target function weights or algorithm parameters to continuously optimize the collaborative strategy in dynamic grid environment.

6. The low voltage distribution network distributed photovoltaic storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 5, characterized in that: The constraints include the maximum output power constraint of photovoltaic units, the charging and discharging capacity limit of energy storage systems, the line load capacity limit, and the stability constraint that the grid voltage deviation must be controlled within ±5%.

7. The low voltage distribution network distributed photovoltaic storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 1, characterized in that, The formula for power compensation is as follows: ； in: This is the power compensation value for the energy storage system; , These are the starting and ending points of the time integration, respectively; The number of distributed photovoltaic units or energy storage systems; For the first Each unit at time... The power fluctuation value is collected in real time by the sensor; For the first The voltage difference of each unit during the fluctuation period; It is the attenuation factor; For dynamic fluctuation functions, simulate the first... Power output fluctuations of individual units; For the first The power fluctuation angular frequency of each unit; For the first Phase offset of each unit; This represents the average voltage value of the system over the time period. The rate of change of current; This is the time decay factor; This is the weight decay factor for historical data; > 0 indicates that the energy storage system output power is compensated; = 0, indicating that the power fluctuation is within the allowable range and no compensation is needed; <0 indicates that the energy storage system absorbs excess power and performs negative compensation.

8. The low voltage distribution network distributed photovoltaic storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 1, characterized in that: Photovoltaic units dynamically adjust output power according to optimal collaborative strategy and current grid state, including the following steps: Photovoltaic units collect real-time data related to output power through built-in sensors, including voltage, current, temperature, and radiation intensity, while receiving shared data from the grid or energy storage devices; Using distributed intelligent agents, real-time assessment of adjustment needs based on current grid voltage state and optimal collaborative strategy: reduce output power first in overvoltage, increase output power or request energy storage compensation in undervoltage, dynamically fine-tune power to suppress fluctuations in voltage fluctuations, and ensure voltage stability within ±5% of the allowable range; According to the optimal coordination strategy, combined with the operating state of the photovoltaic unit, a specific adjustment value is calculated The adjustment value is dynamically generated by an optimization algorithm based on power grid demand and device characteristics adjusting a power inverter output signal of a photovoltaic unit according to the calculated adjustment value controlling the power output in real time to meet grid stability requirements Through real-time monitoring of the adjusted grid state, if the deviation has not returned to the normal range, trigger the next round of dynamic adjustment.

9. The low voltage distribution network distributed photovoltaic storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 8, characterized in that: adjustment value The calculation formula is: ； wherein is the maximum power output of the photovoltaic unit; is a dynamic coefficient determined in the optimization strategy; is a response sensitivity adjustment coefficient; is the current voltage deviation, i.e. the difference between the voltage value of a measurement point in the actual power grid operation and the target voltage value.

10. The low voltage distribution network distributed photovoltaic storage autonomous alternating control method with voltage stability as the core of multi-objective optimization according to claim 1, characterized in that: Alternating start-stop mechanism includes setting start-stop rules, start-stop priority sorting, dynamic strategy adjustment, collaborative scheduling mechanism, exception handling mechanism, and data recording and optimization.

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