Power distribution network light storage charging control method, device and equipment based on intensity-marginal

Abstract

The application provides a power distribution network light storage charging control method, device and equipment based on intensity-marginal, and relates to the technical field of power distribution network control. The method comprises the following steps: calculating a power grid intensity index set of the power distribution network based on the operation parameters of the power distribution network; calculating the device life margin of each device based on the operation parameters of the power distribution network, and taking the minimum value in the device life margin of each device as the comprehensive life margin of the power distribution network; determining the power grid intensity-life margin two-dimensional operation partition type of the power distribution network according to the power grid intensity index set and the comprehensive life margin; constructing a multi-objective optimization function according to the power grid intensity-life margin two-dimensional operation partition type, and determining the network construction control parameter of the power distribution network; and generating the photovoltaic, energy storage and charging power instructions of the power distribution network based on the network construction control parameter and the constraint condition. The application can make the light storage charging system consider the short-time support capacity and long-term operation reliability under variable working condition.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control technology, and in particular to a power distribution network photovoltaic-storage-charging control method, device and equipment based on strength-margin. Background Technology

[0002] With the large-scale integration of distributed photovoltaic (PV), electrochemical energy storage, and electric vehicle (EV) charging facilities into distribution networks, integrated PV-storage-charging systems are gradually becoming an important flexible regulation resource in regional distribution networks. Existing control methods for PV-storage-charging systems mostly adopt a grid-following control architecture, with control objectives primarily focused on active power point tracking, peak shaving and valley filling, charging load management, and local voltage regulation. In scenarios with high grid strength at the connection point, this control method can meet routine operation requirements. However, when the PV-storage-charging system is connected to a weak grid area or a distribution terminal with high line impedance, the grid-following control's tendency to track external voltage and frequency makes it difficult to provide effective active support to the connection point during grid disturbances, easily leading to problems such as voltage drops, frequency shifts, and control coupling instability.

[0003] To address the aforementioned issues, some existing technologies have introduced grid-based control strategies to enable converters to exhibit voltage source characteristics, providing voltage and frequency references for weak grids. However, these methods typically operate with fixed control parameters, failing to adjust these parameters based on real-time changes in grid strength at the connection point. This leads to a mismatch between support capacity and grid demand when grid conditions change, causing minor issues like voltage overruns or frequency fluctuations, and more serious problems like system oscillations or even grid disconnection. Furthermore, inverters and converters in photovoltaic-storage-charging systems endure frequent power fluctuations and thermal cycling shocks during long-term operation. Especially under conditions such as grid support, reactive power compensation, and charge / discharge switching, the thermal stress accumulation of power devices accelerates significantly, leading to faster equipment aging. However, existing control methods, when allocating power commands, only consider conventional operational constraints such as voltage, power, and energy storage state of charge, lacking real-time sensing and proactive protection mechanisms for the lifespan margin of power devices. This results in some devices bearing high-intensity support tasks beyond their health limits for extended periods, accelerating equipment aging and reducing the overall long-term operational reliability of the system. Even worse, the increased demand for grid support and the accelerated decline in equipment lifespan may create a positive feedback loop. In other words, stronger grid support is needed in weak grid scenarios, but stronger support accelerates the wear and tear on equipment. The two factors reinforce each other and may eventually lead to the system losing its support capability due to equipment failure at the moment when the grid needs support the most. Summary of the Invention

[0004] This invention provides a method, apparatus, and equipment for controlling photovoltaic-storage-charging systems in distribution networks based on strength-margin, in order to solve the problem that it is difficult to balance short-term support capacity and long-term operational reliability in the control of photovoltaic-storage-charging systems in distribution networks.

[0005] In a first aspect, embodiments of the present invention provide a power distribution network photovoltaic-storage-charging control method based on strength-margin, comprising: Calculate the power grid strength index set of the distribution network based on the operating parameters of the distribution network; The device life margin of each device is calculated based on the operating parameters of the distribution network, and the minimum value of the device life margin is taken as the overall life margin of the distribution network. Based on the power grid strength index set and comprehensive lifetime margin, determine the two-dimensional operation zoning type of the distribution network based on power grid strength and lifetime margin. A multi-objective optimization function is constructed based on the two-dimensional operation zoning type of power grid strength-life margin, and the network construction control parameters of the distribution network are determined. Based on the grid control parameters and constraints, photovoltaic, energy storage and charging power commands for the distribution network are generated.

[0006] Secondly, embodiments of the present invention provide a power distribution network photovoltaic-storage-charging control device based on strength-margin, comprising: The strength calculation module is used to calculate the set of power grid strength indicators of the power distribution network based on the operating parameters of the power distribution network; The margin calculation module is used to calculate the device lifetime margin of each device based on the operating parameters of the distribution network, and take the minimum value of the lifetime margin of each device as the comprehensive lifetime margin of the distribution network. The partitioning determination module is used to determine the two-dimensional operation partitioning type of the distribution network based on the power grid strength index set and the comprehensive lifetime margin. The parameter determination module is used to construct a multi-objective optimization function based on the two-dimensional operation partition type of the power grid strength-life margin, and to determine the network construction control parameters of the distribution network. The instruction generation module is used to generate photovoltaic, energy storage and charging power instructions for the distribution network based on the network control parameters and constraints.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0008] The present invention provides a distribution network photovoltaic-storage-charging control method, device, and equipment based on strength-margin. By collecting operating parameters of the distribution network, calculating the grid strength index set and comprehensive lifetime margin, and constructing a two-dimensional operating partition based on grid strength and lifetime margin, it can simultaneously reflect the support capacity of the external grid and the health status of internal equipment. Based on this, it adaptively determines grid construction control parameters according to different partition types and generates power commands for photovoltaic, energy storage, and charging facilities under various operating constraints, executing voltage source-type grid construction control. This enables the distribution network photovoltaic-storage-charging system to dynamically adjust the grid construction support strength according to changes in grid strength, solving the problem of mismatch between support strength and grid demand when fixed control parameters change grid conditions, and reducing the risk of voltage exceedance and system oscillation. Simultaneously, it incorporates equipment lifetime margin into power allocation constraints, allowing for reasonable power allocation based on the remaining lifetime status of each device when grid construction support demand increases, avoiding accelerated aging of individual devices due to long-term high-intensity support tasks, and extending the overall service life of the system. Through the synergy of grid strength perception and lifetime margin assessment, the distribution network photovoltaic-storage-charging system can balance short-term support capacity and long-term operational reliability under varying operating conditions. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the implementation of a power distribution network photovoltaic-storage-charging control method based on strength-margin, according to an embodiment of the present invention. Figure 2 This is an architecture diagram of the adaptive grid construction control system for photovoltaic-storage-charging distribution networks provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the implementation of a power distribution network photovoltaic-storage-charging control method based on strength-margin, according to another embodiment of the present invention. Figure 4 This is a flowchart illustrating the implementation of online power grid strength identification provided in this embodiment of the invention. Figure 5 This is a flowchart illustrating the implementation of online lifetime margin assessment provided in an embodiment of the present invention; Figure 6 This is a control logic diagram of the adaptive adjustment relationship between two-dimensional operation partitions and network parameters provided in the embodiments of the present invention; Figure 7 This is a flowchart illustrating the implementation of optical-storage-charging coordinated power allocation and closed-loop update provided in an embodiment of the present invention. Figure 8 This is a schematic diagram of the structure of the power distribution network photovoltaic-storage-charging control device based on strength-margin provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0010] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0011] See Figure 1 The document illustrates a flowchart of the implementation of the intensity-margin-based distribution network photovoltaic-storage-charging control method provided by an embodiment of the present invention, detailed below: Step 101: Calculate the power grid strength index set of the distribution network based on the operating parameters of the distribution network.

[0012] In this embodiment, the architecture of the photovoltaic-storage-charging adaptive grid control system where the distribution network is located can be as follows: Figure 2 As shown. The operating parameters of the distribution network include grid connection point voltage, current, frequency, phase angle, active power, reactive power, photovoltaic output, energy storage state of charge, charging load demand, power device temperature, and ambient temperature data, forming the operating state vector of the photovoltaic-storage-charging system. The edge controller of the distribution network's photovoltaic-storage-charging system synchronously collects the operating parameters of the access point and various devices within the station according to a preset sampling period. These operating parameters are collected in the data buffer of the edge controller through the station's communication network, forming a state vector for subsequent calculations. Since the photovoltaic inverters, energy storage converters, and charging facilities in the photovoltaic-storage-charging system generate continuous power fluctuations during normal operation, these fluctuations themselves contain grid-side impedance information at the access point. Therefore, grid strength sensing can be completed using existing measurement data without the need for additional hardware measurement devices. If the amplitude of natural disturbances is insufficient to ensure identification accuracy, the edge controller can also instruct the energy storage converter to inject small active or reactive power probe disturbances into the grid connection point to actively excite measurable voltage and current responses.

[0013] The running state vector is represented as: in, and These are the grid connection point voltage and current, respectively. For frequency, The phase angle, , The active and reactive power of the photovoltaic inverter. , For the active and reactive power of the energy storage converter, In the state of energy storage charge, , For the power of charging facilities, , , These are the junction temperature of the power device, the hot spot temperature of the capacitor, and the ambient temperature. All of these quantities are time-varying variables, and each term in the vector is estimated in each control cycle through the corresponding sensor or the internal state of the converter.

[0014] After obtaining the above operating parameters, the controller extracts the grid connection point voltage, grid connection point current, and changes in power before and after the power disturbance. Based on the grid connection point voltage, current, and power disturbance information, the grid strength at the access point of the photovoltaic-storage-charging system can be identified online, resulting in a set of grid strength indicators including equivalent grid impedance, short-circuit ratio, voltage stiffness, reactive power-voltage sensitivity, and identification confidence level.

[0015] Step 102: Calculate the device lifetime margin of each device based on the operating parameters of the distribution network, and take the minimum value of the lifetime margin of each device as the comprehensive lifetime margin of the distribution network.

[0016] In this embodiment, based on the operating power, current, switching status, and temperature information of the photovoltaic converter, energy storage converter, and charging converter, an online electro-thermal-lifetime assessment model for power devices and energy storage units is established to obtain the lifetime margins of IGBTs, DC bus capacitors, and energy storage batteries. During long-term operation, the power devices in the photovoltaic-energy storage-charging system continuously endure electrical and thermal stresses, and their aging rate is closely related to the junction temperature fluctuation amplitude, average temperature, and power cycle count. The aging of the DC bus capacitor is mainly affected by the capacitor hotspot temperature, while the lifetime degradation of the energy storage battery is directly related to the charge / discharge rate, state-of-charge operating range, and cycle depth. The edge controller calculates the cumulative lifetime damage of the above three types of key components item by item based on the real-time operating data of each converter and converts them into remaining lifetime margins.

[0017] Step 103: Determine the two-dimensional operation zone type of the distribution network based on the power grid strength index set and the comprehensive life margin.

[0018] In this embodiment, after the controller simultaneously obtains the power grid strength index set and the comprehensive lifetime margin, the operation partitioning discrimination module uses both as inputs to perform a two-dimensional operation partitioning determination. The basic logic of this determination is to comprehensively position the system's operational status from two dimensions: external power grid support conditions and internal equipment health status. Power grid strength reflects the external distribution network's support capability for the voltage and frequency at the grid connection point of the photovoltaic-storage-charging system, while the comprehensive lifetime margin reflects the remaining safe operating time potential of the photovoltaic-storage-charging system's own equipment under continuous operating conditions.

[0019] The controller categorizes the system into five zones based on grid strength and overall lifespan margin: a strong grid lifespan sufficient zone, a medium grid coordination zone, a weak grid construction support zone, a lifespan-limited protection zone, or a weak grid lifespan-limited emergency support zone. The strong grid lifespan sufficient zone indicates ample external grid support and good equipment health, allowing for free scheduling according to economic operating conditions. The medium grid coordination zone suggests limited external support but relatively good equipment health, requiring appropriate consideration of voltage regulation and power smoothing. The weak grid construction support zone indicates that the external grid can no longer provide sufficient voltage and frequency references for the grid connection point, necessitating the photovoltaic-storage-charging system itself to establish voltage source characteristics to support the electrical quantities at the grid connection point. The lifespan-limited protection zone signifies that some critical equipment is nearing its lifespan limit, requiring proactive reduction of operating intensity even under relatively good external grid conditions. The weak grid lifespan-limited emergency support zone represents extreme operating conditions, requiring the system to maintain minimum voltage source support functionality even under poor equipment health. These five zones constitute a complete state coverage from optimal to worst, ensuring a clear control strategy for the system under any operating combination.

[0020] Step 104: Construct a multi-objective optimization function based on the two-dimensional operation zoning type of power grid strength-life margin, and determine the network construction control parameters of the distribution network.

[0021] In this embodiment, after the operating partition type is determined, the adaptive network control parameter module performs optimization calculations of the control parameters accordingly. For different operating partition types, and in conjunction with the applicable strategies for each partition type, different weights are set for each optimization objective to adaptively determine the network control parameters of the optical storage and charging system.

[0022] Network control parameters include at least virtual inertia, virtual damping, active-frequency droop coefficient, reactive-voltage droop coefficient, virtual impedance, current limiting threshold, power change rate constraint, and lifetime protection derating factor. Virtual inertia is used to adjust the inertial response strength of the converter when the frequency changes; virtual damping is used to suppress power oscillations; the active-frequency droop coefficient and reactive-voltage droop coefficient determine the converter's active power response sensitivity to frequency deviation and reactive power response sensitivity to voltage deviation, respectively; virtual impedance is used to adjust the converter's output impedance characteristics to improve stability under weak grid conditions; the current limiting threshold limits the converter's maximum output current during transient processes; and the lifetime protection derating factor is used to proactively reduce the upper limit of its operating power when the equipment's lifespan is limited.

[0023] Step 105: Based on the grid control parameters and constraints, generate photovoltaic, energy storage and charging power commands for the distribution network.

[0024] In this embodiment, as Figure 3As shown, under the conditions of satisfying power flow constraints, voltage constraints, current constraints, energy storage state of charge constraints, charging service constraints, and lifetime margin constraints, active and reactive power commands for photovoltaic inverters, energy storage converters, and charging facilities are generated in a coordinated manner. After implementing the generated commands, the operating status parameters of the distribution network are re-acquired, and steps 101-105 are repeated until the operating requirements are met.

[0025] The power flow constraints require that the algebraic sum of the power of each converter and the power exchanged at the grid connection point satisfy a power balance relationship. Voltage constraints require that the voltage amplitude of each node in the system be maintained within the upper and lower limits allowed by the equipment. Current constraints prevent feeder overload. Energy storage state of charge constraints prevent battery overcharging or over-discharging. Charging service constraints ensure that charging facilities participate in regulation while meeting basic charging needs. Lifetime margin constraints ensure that the overall lifetime margin does not fall below the safety threshold due to this power command. The voltage source type grid control of the photovoltaic-storage-charging system is executed according to the grid control parameters and power command, enabling the photovoltaic-storage-charging system to achieve voltage support, frequency support, reactive power support, and flexible adjustment of charging load under different grid strengths.

[0026] This invention collects operating parameters of the distribution network, calculates the set of grid strength indicators and comprehensive lifetime margin, and constructs a two-dimensional operating partition based on grid strength and lifetime margin. This partition simultaneously reflects the support capacity of the external grid and the health status of internal equipment. Based on this, grid construction control parameters are adaptively determined according to different partition types, and power commands for photovoltaic, energy storage, and charging facilities are generated under various operating constraints. Voltage source-type grid construction control is executed, enabling the distribution network's photovoltaic-energy storage-charging system to dynamically adjust its grid support strength according to changes in grid strength. This solves the problem of mismatch between support strength and grid demand when fixed control parameters change grid conditions, reducing the risk of voltage exceedances and system oscillations. Simultaneously, equipment lifetime margin is incorporated into power allocation constraints. When grid support demand increases, power can be rationally allocated according to the remaining lifetime of each device, preventing accelerated aging of individual devices due to long-term high-intensity support tasks and extending the overall lifespan of the system. Through the synergy of grid strength perception and lifetime margin assessment, the distribution network's photovoltaic-energy storage-charging system can balance short-term support capacity and long-term operational reliability under varying operating conditions.

[0027] In one possible implementation, a set of grid strength indices for the distribution network is calculated based on the network's operating parameters, including: Extract the voltage, current and power changes at the grid connection point of the distribution network before and after the power disturbance, and estimate the equivalent grid impedance at the connection point; Calculate the short-circuit capacity and short-circuit ratio based on the equivalent grid impedance; Calculate the reactive power-voltage sensitivity and voltage stiffness indices based on the grid connection point voltage before and after the disturbance.

[0028] In this embodiment, as Figure 4 As shown, the steps for online power grid strength identification include: S21, extract the natural power disturbances during the operation of the photovoltaic-storage-charging system, or inject small active and reactive power test disturbances by the energy storage converter. Natural power disturbances include power fluctuations in photovoltaic output caused by changes in weather conditions, sudden load changes caused by electric vehicles connecting to or disconnecting from charging, and power steps generated when the energy storage system switches between charging and discharging according to energy management strategies.

[0029] S22. Estimate the equivalent grid impedance at the connection point based on the changes in voltage, current, and power before and after the disturbance. The calculation formula is: in, This represents the change in the voltage phasor at the grid connection point before and after the disturbance. These are the changes in the phasor current at the grid connection point before and after the disturbance; both are complex phasors. That is, the estimated equivalent grid impedance, which is actually part of the circuit. Represents the equivalent resistance of the power grid, with the imaginary part. This represents the equivalent reactance of the power grid. Based on Thevenin's equivalence principle, this estimation method treats the distribution network portion other than the disturbance source as a linear network consisting of equivalent voltage sources and equivalent impedances connected in series. Under conditions where the disturbance amplitude is sufficiently small, this approximation has adequate engineering accuracy.

[0030] S23. Calculate the short-circuit capacity and short-circuit ratio based on the equivalent grid impedance. The calculation formula is: in, The three-phase short-circuit capacity at the grid connection point represents the apparent power that the grid can provide when a three-phase metallic short circuit occurs at the grid connection point, and the unit is megavolt-amperes (MVA). This is the effective value of the line voltage at the grid connection point. This is the magnitude of the equivalent grid impedance. The short-circuit ratio is the ratio of short-circuit capacity to the rated capacity of the photovoltaic-storage-charging system. The ratio of the short-circuit ratio to the grid connection. A larger short-circuit ratio indicates a stronger grid, allowing the converter to operate stably with grid-based control; a smaller short-circuit ratio indicates a weaker grid, requiring the converter to provide voltage source-type grid support.

[0031] S24, calculate the reactive power-voltage sensitivity and voltage stiffness indices. The calculation formula is: in, This represents the change in voltage at the grid connection point before and after the reactive power disturbance. The ratio of the two is the corresponding change in reactive power. This is known as reactive power-voltage sensitivity, which reflects the voltage fluctuation amplitude caused by a unit change in reactive power. The larger the value, the more easily the voltage is affected by reactive power disturbances. Voltage stiffness, taken as the reciprocal of sensitivity, characterizes the power grid's ability to resist voltage fluctuations; the larger the value, the stronger the power grid's voltage support. This is a small positive constant used to prevent the stiffness from approaching infinity when the denominator reaches zero and the sensitivity approaches zero. It is typically set to a value of [value missing]. to A sufficiently small positive number between these two values ​​ensures that the effective range of the stiffness index is not affected during calculation, while also preventing numerical calculation anomalies.

[0032] In one possible implementation, the device lifetime margin of each device is calculated based on the operating parameters of the distribution network, including: Calculate the power device losses based on the current, voltage, modulation ratio, and switching frequency of the photovoltaic inverter, energy storage converter, and charging converter. By inputting the power loss into the thermal network model, the junction temperature sequence of the power device is obtained; Online rainflow counting was performed on the junction temperature sequence of power devices to extract thermal cycle amplitude, average temperature and number of cycles, thus obtaining a thermal cycle set. Calculation of cumulative lifetime damage of power devices based on thermal cycling ensemble; The life margins of IGBTs, DC bus capacitors, and energy storage batteries are calculated based on the cumulative life damage of power devices.

[0033] In this embodiment, as Figure 5 As shown, the power device lifetime margin assessment includes: S31 calculates the power device losses based on the current, voltage, modulation ratio, and switching frequency of the photovoltaic inverter, energy storage converter, and charging converter. The calculation formula is: in, Conduction loss refers to the Joule heat loss generated by the conduction voltage drop and conduction current of a power device when it is in the conduction state. Its magnitude is proportional to the square of the effective value of the phase current and the conduction duty cycle. Switching losses refer to the energy loss caused by the overlap of voltage and current during each turn-on and turn-off process of a power device. Their magnitude is related to the DC bus voltage, switching frequency, and the rate of current change during the switching process. These two factors together constitute the total power loss of the power device. This power loss is the heat input source for subsequent thermal network model calculations of junction temperature.

[0034] S32, input the power loss into the thermal network model to obtain the junction temperature of the power device. The calculation formula is: in, This represents the equivalent thermal impedance of the power device. This is the Laplace operator. The thermal impedance model uses a Cauer-type or Foster-type equivalent thermal network to describe the thermal resistance and thermal capacity effects experienced during the transfer of power loss from the chip junction region to the external environment. Its parameters can be obtained by fitting the transient thermal impedance curves provided in the power device datasheet. The ambient temperature is collected in real time by a temperature sensor installed near the converter heat sink. This is the calculated instantaneous junction temperature value of the power device.

[0035] S33 performs online rainflow counting on the junction temperature sequence to extract thermal cycling amplitude, average temperature, and cycle number. This is represented as: in, For the first The amplitude of a thermal cycle is the temperature difference between the junction temperature and the peak value during that cycle. For the first The average junction temperature of a thermal cycle, which is the arithmetic mean of the peak and valley values ​​of the junction temperature in that cycle; For the current statistical period, the [number]th The cumulative number of thermal cycles. Rainflow counting is a cycle counting method used for material fatigue analysis. Its core principle is to decompose irregular stress or temperature time history curves into several complete stress or temperature cycles. Each cycle is uniquely characterized by two parameters: its amplitude and its mean. It can truly reflect the thermal cycle damage accumulation process of the converter under random power fluctuation conditions.

[0036] The cumulative lifetime damage of power devices is calculated based on thermal cycling. The calculation formula is as follows: in, For IGBT at any time The cumulative lifetime damage is between 0 and 1. When the cumulative damage reaches 1, it means that the device has reached the end of its expected lifetime. This represents the total number of cycle types contained in the current thermal cycle set. For the first The number of failure cycles corresponding to thermal cycling conditions, i.e., the number of power devices at an amplitude of The average temperature is The upper limit of the number of cycles a device can withstand under thermal cycling is obtained from its power cycle life curve, which is typically calibrated by the device manufacturer through accelerated aging tests. This curve reflects the inverse relationship between the thermal cycling amplitude and the number of failure cycles; that is, the larger the thermal cycling amplitude, the fewer cycles it can withstand. This expression is the Miner linear cumulative damage criterion, whose basic assumption is that the fatigue damage generated by thermal cycles of different amplitudes can be linearly added together, and the device fails due to fatigue when the total cumulative damage reaches 1.

[0037] IGBT lifetime margin is expressed as: in, This is the cumulative damage limit value for the IGBT, usually set to 1, representing the cumulative damage threshold at which the device reaches the end of its design life. A lifetime margin greater than 0 indicates that the device still has remaining life, while a value equal to 0 indicates that the life has been exhausted.

[0038] The DC bus capacitor life margin is expressed as follows: in, The used equivalent life of the DC bus capacitor is the equivalent operating time under rated temperature conditions, calculated based on the current hot spot temperature. This refers to the designed lifespan of the capacitor under rated operating conditions. The rate of capacitor lifespan degradation is exponentially related to the hot spot temperature, typically following the Arrhenius accelerated aging model, where the lifespan is halved for every certain temperature increase.

[0039] The life margin of energy storage batteries is expressed as: in, The cumulative lifespan damage of energy storage batteries can be calculated using an equivalent cycle life model based on the battery's charge-discharge cycle depth, state-of-charge operating range, and temperature. This represents the battery's lifespan damage limit, corresponding to the cumulative damage when the battery capacity decays to 80% of its rated capacity.

[0040] The overall lifetime margin of a photovoltaic-storage-charging system is taken as the minimum value among the three factors: The overall lifetime margin reflects the remaining lifetime ratio of the most vulnerable critical component in the photovoltaic-storage-charging system. This is because the failure of any critical component will cause the converter containing that component to take off, thereby affecting the grid support capability of the entire system.

[0041] In one possible implementation, the two-dimensional operation zone type of the distribution network based on the power grid strength index set and the comprehensive lifetime margin is determined, including: The power grid strength of the distribution network is determined based on the power grid strength index set; If the power grid strength is strong and the comprehensive life margin is greater than or equal to the first margin threshold, then the two-dimensional operation zoning type of the distribution network is determined to be a strong grid life margin sufficient zone. If the grid strength is medium and the comprehensive lifetime margin is greater than or equal to the second margin threshold, then the grid strength-life margin two-dimensional operation zoning type of the distribution network is determined to be a medium grid coordination zone; wherein, the second margin threshold is less than the first margin threshold. If the power grid strength is weak and the comprehensive life margin is greater than or equal to the second margin threshold, then the two-dimensional operation zoning type of the distribution network based on the power grid strength-life margin is determined to be the weak network construction support zone. If the grid strength is strong or medium and the comprehensive lifetime margin is less than the second margin threshold, then the grid strength-life margin two-dimensional operation zoning type of the distribution network is determined to be a lifetime-limited protection zone. If the power grid strength is weak and the overall life margin is less than the second margin threshold, then the two-dimensional operation zone type of the distribution network, which is defined as the weak network life-limited emergency support zone, is determined.

[0042] In this embodiment, the controller compares the short-circuit ratio from the grid strength index set with preset high and low short-circuit ratio thresholds to determine the grid strength level of the current access point. The high short-circuit ratio threshold is typically set to 3.0, indicating that the grid has sufficient active and reactive power support above this value; the low short-circuit ratio threshold is typically set to 2.0, indicating that the grid strength is significantly weak below this value, requiring active support from a voltage source converter; the range between these two values ​​represents a medium grid strength range. When the short-circuit ratio is greater than or equal to the high threshold, it is determined to be a strong grid level; when the short-circuit ratio is between the high and low thresholds, it is determined to be a medium grid level; when the short-circuit ratio is lower than the low threshold, it is determined to be a weak grid level. When the short-circuit ratio is near the threshold boundary and the identification confidence is low, the controller can also refer to the voltage stiffness index for auxiliary correction of the strength level—if the voltage stiffness index continues to decrease while the short-circuit ratio is only slightly higher than the low threshold, the grid strength level is lowered by one level to ensure the conservatism of the control strategy.

[0043] After determining the power grid strength level, the controller further combines this level result with the comprehensive lifespan margin to determine the specific two-dimensional operating zone type according to the following rules. The controller compares the comprehensive lifespan margin with a preset first margin threshold and a second margin threshold, where the second margin threshold is less than the first margin threshold. The first margin threshold is typically set to 0.5, indicating that the equipment's lifespan has not yet exceeded half and it is in a healthy state; the second margin threshold is typically set to 0.2, indicating that the equipment is nearing the end of its lifespan and continuing to operate at rated conditions will face a high risk of failure. When the grid strength is at the strong grid level and the comprehensive lifespan margin is greater than or equal to the first margin threshold, it is determined to be a strong grid lifespan sufficient zone; when the grid strength is at the medium grid level and the comprehensive lifespan margin is greater than or equal to the second margin threshold, it is determined to be a medium grid coordination zone; when the grid strength is at the weak grid level and the comprehensive lifespan margin is greater than or equal to the second margin threshold, it is determined to be a weak grid construction support zone; when the grid strength is at the strong grid level or the medium grid level and the comprehensive lifespan margin is less than the second margin threshold, it is determined to be a lifespan-limited protection zone; when the grid strength is at the weak grid level and the comprehensive lifespan margin is less than the second margin threshold, it is determined to be a weak grid lifespan-limited emergency support zone.

[0044] Correspondingly, such as Figure 6 As shown, the above-mentioned judgment rule based on power grid strength level can be equivalently expressed at the specific threshold comparison level as follows: when the short-circuit ratio is greater than or equal to the high short-circuit ratio threshold and the comprehensive life margin is greater than or equal to the first margin threshold, it is judged as a strong grid lifespan sufficient zone; when the short-circuit ratio is between the low short-circuit ratio threshold and the high short-circuit ratio threshold and the comprehensive life margin is greater than or equal to the second margin threshold, it is judged as a medium grid coordination zone; when the short-circuit ratio is lower than the low short-circuit ratio threshold and the comprehensive life margin is greater than or equal to the second margin threshold, it is judged as a weak grid construction support zone; when the comprehensive life margin is less than the second margin threshold and the short-circuit ratio is greater than or equal to the low short-circuit ratio threshold, it is judged as a lifespan-limited protection zone; when the short-circuit ratio is lower than the low short-circuit ratio threshold and the comprehensive life margin is less than the second margin threshold, it is judged as a weak grid lifespan-limited emergency support zone. Here, the high short-circuit ratio threshold, the low short-circuit ratio threshold, the first margin threshold, and the second margin threshold are all pre-set constant thresholds.

[0045] The five zones described above constitute a complete coverage of states from optimal to worst. The "strong grid lifespan sufficient" zone indicates ample external grid support and good equipment health, allowing the system to be freely scheduled according to economic operating modes. The "medium grid coordination" zone suggests limited external support but relatively good equipment health, requiring appropriate consideration of voltage regulation and power smoothing. The "weak grid support" zone indicates that the external grid can no longer provide sufficient voltage and frequency references for the grid connection point, necessitating the photovoltaic-storage-charging system itself to establish voltage source characteristics to support the electrical quantities at the grid connection point. The "lifespan-limited" protection zone means that some critical equipment is nearing its service life limit, and even if external grid conditions are still acceptable, operational intensity should be proactively reduced. The "weak grid lifespan-limited emergency support" zone represents extreme operating conditions, requiring the system to maintain a minimum level of voltage source support even under poor equipment health. These five zones constitute a complete coverage of states from optimal to worst, ensuring that the system has a clear control strategy to follow under any operating combination.

[0046] In one possible implementation, a multi-objective optimization function is constructed based on the two-dimensional operation zoning type of power grid strength-life margin, and the network construction control parameters of the distribution network are determined, including: The weights of each optimization objective are determined based on the two-dimensional operation zoning type of power grid strength-life margin and the preset weight mapping relationship; A multi-objective optimization function is constructed based on the weights of each optimization objective; the multi-objective optimization function is as follows: in, For the voltage deviation at the grid connection point, For frequency deviation, The increase in lifespan per unit time. The cost of solar power curtailment The cost of charging service deviations to These are the weighting coefficients.

[0047] In this embodiment, the controller first queries a preset weight mapping table based on the current partition type. This mapping table configures a set of weight coefficients for each of the five partitions.

[0048] The weight mapping table is constructed following a priority-oriented principle, meaning different operating zones correspond to different primary control objectives: the primary objective in the strong grid lifespan zone is economical operation and charging service quality; the primary objective in the medium grid coordination zone is a balance between voltage regulation and power smoothing; the primary objective in the weak grid support zone is stable voltage and frequency support; the primary objective in the lifespan-limited protection zone is to slow down equipment aging; and the primary objective in the weak grid lifespan-limited emergency support zone is to maximize equipment protection while maintaining minimum stability. The relative importance of each optimization objective within each zone is expressed through the numerical relationship of the weight coefficients. Specifically, in the strong grid lifespan zone, the weight coefficients are set to... =0.1、 =0.1、 =0.1、 =0.4、 =0.3 indicates that voltage and frequency deviations have lower priority than economic indicators, and the controller prioritizes ensuring photovoltaic power consumption and charging services. In the medium grid coordination area, the weight is set to... =0.25、 =0.25、 =0.15、 =0.2、 =0.15, the weights of each objective tend to be balanced, reflecting the characteristics of coordinated control. In the weak network support area, the weights are set to... =0.35、 =0.35、 =0.1、 =0.1、 With a weighting of 0.1, the weights of voltage and frequency deviations are significantly increased, becoming the primary optimization objective, while lifetime damage and economic indicators are relegated to secondary positions. In the lifetime-limited protected area, the weights are set to... =0.1、 =0.1、 =0.5、 =0.15、 =0.15, with the highest weight assigned to the incremental lifespan damage per unit time, the controller will prioritize operating modes with gradual changes in thermal stress, even if this may cause a certain increase in voltage or frequency deviation. In emergency support areas with limited network lifespan, the weight is set to... =0.3、 =0.3、 =0.3、 =0.05、 =0.05, stability, lifespan and economy are given high and balanced weights, but photovoltaic curtailment and charging service deviation are no longer the main considerations.

[0049] The numerical design of the aforementioned weighting coefficients also follows the principle of normalization and coordination, meaning that the sum of the five weighting coefficients is always equal to 1. This setting ensures that the dimensional differences of each item in the multi-objective optimization function are reasonably coordinated—the unit of voltage deviation is volts, the unit of frequency deviation is hertz, the increment of lifetime damage is a dimensionless percentage, and the costs of abandoned power and charging service deviations are all converted to per-unit values ​​with the same base value as the denominator. The specific values ​​of the weighting coefficients are determined through parameter sensitivity analysis under different typical operating conditions using offline simulation. The core design basis is that for any operating partition, the weight value of the primary optimization objective must be at least twice the weight value of the secondary objective to form a clear objective priority gradient, enabling the optimization algorithm to have a clear and differentiated optimization preference in different partitions. The aforementioned weight mapping table is pre-stored in the edge controller. During system operation, it is fine-tuned online based on long-term operating data through a cloud-edge collaboration mechanism. When events such as voltage exceeding limits or lifespan damage exceeding expectations occur repeatedly in a certain partition, the cloud platform makes a small adjustment to the weight combination corresponding to that partition and sends the updated mapping table to the edge controller, enabling the control strategy to adapt to the slow changes in grid characteristics and equipment aging trends.

[0050] Optimization variables include: in, This is a virtual inertia, simulating the rotor inertia of a synchronous generator, used to adjust the converter's response strength to the rate of frequency change, measured in seconds. This is a virtual damping mechanism used to suppress active power oscillations; the unit is per unit value. The active power-frequency droop factor determines the converter's active power output response sensitivity to frequency deviation. The reactive power-voltage droop coefficient determines the sensitivity of the converter's reactive power output to voltage deviation. , These are virtual impedance parameters, simulating virtual resistance and virtual reactance respectively, used to adjust the output impedance characteristics of the converter to improve stability under weak power grid conditions; The current limiting threshold limits the maximum output current amplitude of the converter during transient processes; This is a lifespan protection derating factor, used to proactively reduce the upper limit of the operating power of equipment when its lifespan is limited. , These are the active and reactive power commands for the photovoltaic inverter, respectively. , These are the active and reactive power commands for the energy storage converter, respectively. This refers to the active power command for charging facilities.

[0051] The optimization process satisfies the following constraints: Power balance constraints: in, For system network loss, , These are the active and reactive power commands exchanged between the grid connection point and the external power grid, respectively, with positive values ​​indicating power absorbed from the grid.

[0052] Voltage constraint: in, For the first in the system The voltage amplitude at each node, and These are the minimum and maximum allowable voltage values, typically taken as 0.93 times and 1.07 times the rated voltage, respectively.

[0053] Line current constraints: in, For the first The effective value of the current in the feeder line, This is the maximum allowable current carrying capacity of the feeder, used to prevent line overload.

[0054] Converter capacity constraints: in, This refers to photovoltaic inverters, energy storage converters, or charging facilities. and For the first Active and reactive power commands for the converter. The rated apparent power of the converter ensures that the converter operates within the safe operating range.

[0055] Energy storage state of charge constraints: in, and These are the minimum and maximum allowable states of charge for the energy storage battery, typically set to 0.1 and 0.9 respectively, to prevent over-discharge or over-charge of the battery.

[0056] Lifetime margin constraints: in, This is the minimum allowable value for the overall life margin, ensuring that the overall life margin of the system does not fall below this safety boundary under any operating conditions.

[0057] Power change rate constraint: in, For the first The upper limit of the power change rate of such equipment limits the maximum adjustment rate of the converter power command per unit time in order to suppress thermal shock and frequency fluctuations caused by power surges.

[0058] Voltage source grid control adopts active-frequency and reactive-voltage coupled control methods: in, Angular frequency commands generated for network control. The rated angular frequency corresponds to 314.16 radians per second at a power frequency of 50 Hz. The measured active power of the converter. This is a reference value for active power. This is the frequency damping coefficient, used to enhance the dynamic response characteristics at frequencies. ω is the rate of change of angular frequency. Voltage amplitude command generated for network control. This is the rated voltage amplitude. The measured reactive power of the converter This is a reference value for reactive power. Active power – frequency droop factor. Reactive power-voltage droop factor The system dynamically adjusts based on grid strength and lifespan margin. When grid strength decreases, virtual damping and virtual impedance are increased, current limiting thresholds are tightened, and the voltage support weight of energy storage converters is increased. When lifespan margin decreases, the power change rate and power load ratio of equipment with high thermal stress are reduced, and support tasks are transferred to equipment with higher lifespan margins.

[0059] In one possible implementation, a multi-objective optimization function is constructed based on the two-dimensional operation zoning type of power grid strength-life margin, and the network control parameters of the distribution network are determined. This also includes: The confidence level of power grid strength identification of the distribution network is calculated based on the effectiveness of disturbance amplitude, measurement noise level, parameter convergence within the sliding window, and equivalent impedance change rate. If the confidence level of power grid strength identification is lower than the preset threshold, the network construction control parameters of the distribution network are determined to be conservative parameters. The formula for calculating the confidence level of power grid strength identification is as follows: in, To identify the confidence level for power grid strength, The effectiveness factor for the disturbance amplitude. To measure the noise factor, Let be the convergence factor of the parameter. The impedance change rate factor, , , , These are the weighting coefficients.

[0060] In this embodiment, The confidence level for identifying power grid strength is set, with a value between 0 and 1. , , , The weight coefficients of the four factors satisfy the following conditions: The weighting coefficients can be determined based on practical engineering experience. Generally, the effectiveness of the disturbance amplitude and the measurement noise level have the most direct impact on identification accuracy, and their weights can be appropriately increased, for example... , , , When the confidence level is greater than or equal to 0.8, the controller considers the current identification result reliable and can adjust the network parameters normally based on the optimization result. When the confidence level is between 0.5 and 0.8, the controller adds a certain safety margin to the optimization result, for example, setting the virtual damping to 1.2 times the optimized value. When the confidence level is less than 0.5, the controller completely abandons the optimization result and instead adopts a conservative parameter combination calibrated in advance through offline simulation. The conservative parameters are characterized by a higher virtual damping value, a lower current limiting threshold value, and a lower upper limit for the power change rate, to ensure that the control system remains stable under the worst identification conditions.

[0061] In one possible implementation, after generating photovoltaic, energy storage, and charging power commands for the distribution network based on grid control parameters and constraints, the following is also included: Based on the execution results of photovoltaic, energy storage, and charging power commands, update the grid strength index set and comprehensive lifetime margin grid strength of the distribution network; Based on the updated set of power grid strength indicators and the comprehensive life margin power grid strength, the network construction control parameters of the distribution network are updated.

[0062] In this embodiment, after the grid control parameters and power commands are issued and executed, the closed-loop update module continuously monitors the execution results and triggers necessary correction actions. For example... Figure 7As shown, after control execution, the module collects real-time data on grid connection point voltage, current, frequency deviation, lifetime damage increment, and grid strength identification error, and performs rolling corrections on the grid strength identification model, lifetime margin assessment model, and grid control parameters. At the end of each control cycle, the module re-collects the actual values ​​of grid connection point voltage, current, and frequency, and calculates the deviation between these values ​​and the command values. If the voltage or frequency deviation exceeds the preset acceptable range, it indicates that the current grid strength identification result may deviate from the actual grid state. The closed-loop update module triggers the online grid strength identification module to recalculate the equivalent grid impedance and short-circuit ratio using the latest collected data, and corrects the operation zoning judgment and grid control parameters accordingly. Simultaneously, the closed-loop update module calculates the actual power change amplitude and junction temperature fluctuation amplitude experienced by each device during the current control cycle and compares it with the lifetime damage increment predicted by the lifetime margin assessment module before execution. If the actual damage is significantly greater than the predicted value, it indicates that the thermal network model or rainflow counting parameters need correction, and this correction is fed back to the parameter update interface of the lifetime margin assessment module. In addition, if slow convergence or failure of the objective function value to drop to the expected level occurs in multiple consecutive control cycles under the current partition, the closed-loop update module records this phenomenon as an optimization strategy mismatch event and attempts to fine-tune the weight coefficients or constraint boundaries in subsequent cycles.

[0063] The cloud platform can update lifetime model parameters, grid strength thresholds, and operating zone boundaries based on historical operational data, and then distribute the updated parameters to the edge grid controllers. For systems configured with the cloud platform, the edge controllers package and upload the parameter changes before and after the correction, deviation records, and optimization mismatch events to the cloud. The cloud platform performs big data analysis based on long-term operational data from multiple sites, periodically corrects lifetime model parameters, grid strength thresholds, and operating zone boundaries, and distributes the updated model parameters to the edge controllers at each site in the form of configuration files, achieving continuous self-optimization through cloud-edge collaboration.

[0064] The present invention will be further illustrated below through specific embodiments.

[0065] Example 1: Implementation of Grid Control for Photovoltaic-Storage-Charging Stations in Regional Distribution Networks In this embodiment, an integrated photovoltaic-storage-charging station is connected to the regional distribution network. This station includes a photovoltaic array, a photovoltaic inverter, energy storage batteries, an energy storage PCS, DC or AC charging facilities, a station-level edge controller, and a cloud management platform. The station is connected to a 10 kV or 0.4 kV distribution network through a public grid connection point.

[0066] The station-level edge controller periodically collects data on grid-connected voltage, current, frequency, phase angle, active power, reactive power, photovoltaic output, energy storage SOC, charging pile power, vehicle access status, power device temperature, and ambient temperature. The sampled data, after time synchronization, forms an operating state vector for subsequent online identification and control calculations.

[0067] During the online grid strength identification process, the controller first extracts natural disturbances caused by photovoltaic power output fluctuations, charging load fluctuations, or energy storage power regulation. When natural disturbances are insufficient to support reliable identification, the energy storage PCS injects small active or reactive power probe disturbances. The controller calculates the equivalent grid impedance based on the changes in voltage and current at the grid connection point before and after the disturbance, and further calculates the short-circuit ratio, voltage stiffness, and reactive-voltage sensitivity.

[0068] When the short-circuit ratio decreases, the equivalent grid impedance increases, or the voltage stiffness decreases, the controller determines that the grid strength at the current connection point has decreased and increases the grid support level. If the identification confidence level is low, the controller adopts conservative parameters, namely, increasing virtual damping, limiting the power change rate, and tightening the converter current limiting threshold.

[0069] During the lifetime margin assessment, the controller calculates power device losses based on the operating power, current, and switching frequency of the photovoltaic inverter, energy storage PCS, and charging converter, and obtains the IGBT junction temperature through a thermal network model. Subsequently, an online rainflow counting method is used to extract the junction temperature thermal cycle, and IGBT lifetime damage is calculated based on a cumulative damage model. For the DC bus capacitor, the controller calculates lifetime consumption based on hotspot temperature and ripple current; for the energy storage battery, the controller calculates battery lifetime damage based on charge / discharge rate, SOC range, temperature, and cycle depth.

[0070] When the life margin of a device falls below a set threshold, the controller marks the device as a life-limited device and reduces its grid support weight and power change rate limit. If the life margin of the energy storage PCS is below the threshold, but the photovoltaic inverter still has reactive power capacity, the photovoltaic inverter will provide reactive power voltage support first; if the photovoltaic inverter has high thermal stress, the energy storage PCS will provide support for a short time, while the charging load will be flexibly reduced.

[0071] Example 2: Adaptive Network Control in Weak Grid Scenarios When online identification shows that the short-circuit ratio is less than the weak network threshold At this point, the system enters the weak grid support zone. The controller increases the voltage source control weight of the energy storage PCS and increases virtual damping and virtual impedance to suppress power oscillations under weak grid conditions.

[0072] Network control takes the following form: Among them, the active power-frequency droop coefficient Reactive power-voltage droop factor The system dynamically adjusts based on grid strength and lifespan margin. When grid strength is weak and lifespan margin is sufficient, the support sensitivity is appropriately increased; when grid strength is weak and lifespan margin is insufficient, the controller prioritizes maintaining voltage stability while reducing thermal stress on the equipment through charging load reduction and photovoltaic power limitation.

[0073] In this scenario, the photovoltaic-storage-charging system no longer aims solely at maximizing photovoltaic absorption or meeting all charging demands. Instead, it allocates power according to the principle of "prioritizing grid stability, followed by lifespan protection, and coordinating and satisfying charging services."

[0074] Example 3: Power Transfer Control in Lifetime-Limited Scenarios When the overall life margin Below the set threshold At this point, the system enters a life-limited protection zone. The controller further identifies specific life-limited components, such as IGBTs, DC bus capacitors, or energy storage batteries.

[0075] If the IGBT life margin is low, reduce the high-frequency power fluctuation of the converter, limit its active and reactive power change rates, and reduce transient current surges by increasing virtual impedance.

[0076] If the DC bus capacitor has a low life margin, reduce its ripple current stress to avoid frequent charge / discharge switching and high-amplitude power pulsation.

[0077] If the life margin of the energy storage battery is low, the energy storage charge / discharge rate and SOC depth variation are limited, and some adjustment tasks are transferred to the reactive power support of the photovoltaic inverter or the flexible adjustment of the charging load.

[0078] Through the above methods, the photovoltaic-storage-charging system can reduce further damage to life-limited equipment without significantly sacrificing grid connection stability.

[0079] Example 4: Emergency Support Scenario with Limited Lifespan Due to Weak Networks When the system simultaneously satisfies and At this time, the system enters the emergency support zone with limited network lifespan. The controller then executes the emergency support strategy: First, the energy storage PCS maintains the minimum necessary grid support to ensure that the voltage and frequency at the grid connection point do not exceed the limits. Second, the photovoltaic inverter provides reactive power support based on the capacity margin, and performs active power limiting when necessary to release reactive capacity. Third, charging facilities can flexibly reduce, postpone, or tier the power supply based on vehicle priority, remaining charging time, and user demand. Fourth, derating should be applied to equipment with limited lifespan to limit peak current and power variation rate; Fifth, when the voltage, frequency, or equipment temperature approaches the protection threshold, an early warning or protection action is triggered.

[0080] This implementation method avoids the problem of "increased demand for weak network support leading to rapid depletion of equipment lifespan" in traditional control, enabling the photovoltaic energy storage and charging system to balance short-term support capability and long-term reliability under extreme operating conditions.

[0081] Example 5: Cloud-Edge Collaborative Update Method The system described in this invention can be implemented collaboratively using an edge controller and a cloud platform. The edge controller is responsible for millisecond- to second-level grid strength identification, lifetime margin assessment, grid parameter updates, and power command issuance. The cloud platform is responsible for historical data storage, lifetime model parameter correction, grid strength threshold updates, and operational strategy evaluation.

[0082] The cloud platform can adjust the thermal network parameters, lifetime model parameters, and operating partition thresholds based on long-term operational data, and then send the adjusted model parameters to the edge controller. The edge controller continues to perform real-time control locally, avoiding communication delays that could affect the stability of the network control.

[0083] Through cloud-edge collaboration, this invention can simultaneously meet the needs of real-time control and long-term model optimization.

[0084] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0085] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0086] Figure 8 A schematic diagram of the structure of the distribution network photovoltaic-storage-charging control device based on strength-margin provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 8 As shown, the distribution network photovoltaic-storage-charging control device 8 based on strength-margin includes: The strength calculation module 81 is used to calculate the set of power grid strength indicators of the power distribution network based on the operating parameters of the power distribution network. The margin calculation module 82 is used to calculate the device lifetime margin of each device based on the operating parameters of the distribution network, and take the minimum value of the lifetime margin of each device as the comprehensive lifetime margin of the distribution network. The partition determination module 83 is used to determine the two-dimensional operation partition type of the distribution network based on the power grid strength index set and the comprehensive lifetime margin. The parameter determination module 84 is used to construct a multi-objective optimization function based on the two-dimensional operation partition type of the power grid strength-life margin, and to determine the network construction control parameters of the distribution network. The instruction generation module 85 is used to generate photovoltaic, energy storage and charging power instructions for the distribution network based on the network control parameters and constraints.

[0087] In one possible implementation, the strength calculation module 81 is specifically used for: Extract the voltage, current and power changes at the grid connection point of the distribution network before and after the power disturbance, and estimate the equivalent grid impedance at the connection point; Calculate the short-circuit capacity and short-circuit ratio based on the equivalent grid impedance; Calculate the reactive power-voltage sensitivity and voltage stiffness indices based on the grid connection point voltage before and after the disturbance.

[0088] In one possible implementation, the margin calculation module 82 is specifically used for: Calculate the power device losses based on the current, voltage, modulation ratio, and switching frequency of the photovoltaic inverter, energy storage converter, and charging converter. By inputting the power loss into the thermal network model, the junction temperature sequence of the power device is obtained; Online rainflow counting was performed on the junction temperature sequence of power devices to extract thermal cycle amplitude, average temperature and number of cycles, thus obtaining a thermal cycle set. Calculation of cumulative lifetime damage of power devices based on thermal cycling ensemble; The life margins of IGBTs, DC bus capacitors, and energy storage batteries are calculated based on the cumulative life damage of power devices.

[0089] In one possible implementation, the partition determination module 83 is specifically used for: The power grid strength of the distribution network is determined based on the power grid strength index set; If the power grid strength is strong and the comprehensive life margin is greater than or equal to the first margin threshold, then the two-dimensional operation zoning type of the distribution network is determined to be a strong grid life margin sufficient zone. If the grid strength is medium and the comprehensive lifetime margin is greater than or equal to the second margin threshold, then the grid strength-life margin two-dimensional operation zoning type of the distribution network is determined to be a medium grid coordination zone; wherein, the second margin threshold is less than the first margin threshold. If the power grid strength is weak and the comprehensive life margin is greater than or equal to the second margin threshold, then the two-dimensional operation zoning type of the distribution network based on the power grid strength-life margin is determined to be the weak network construction support zone. If the grid strength is strong or medium and the comprehensive lifetime margin is less than the second margin threshold, then the grid strength-life margin two-dimensional operation zoning type of the distribution network is determined to be a lifetime-limited protection zone. If the power grid strength is weak and the overall life margin is less than the second margin threshold, then the two-dimensional operation zone type of the distribution network, which is defined as the weak network life-limited emergency support zone, is determined.

[0090] In one possible implementation, the parameter determination module 84 is specifically used for: The weights of each optimization objective are determined based on the two-dimensional operation zoning type of power grid strength-life margin and the preset weight mapping relationship; A multi-objective optimization function is constructed based on the weights of each optimization objective; the multi-objective optimization function is as follows: in, For the voltage deviation at the grid connection point, For frequency deviation, The increase in lifespan damage per unit time. The cost of solar power curtailment The cost of charging service deviations to These are the weighting coefficients.

[0091] In one possible implementation, the parameter determination module 84 is also used for: The confidence level of power grid strength identification of the distribution network is calculated based on the effectiveness of disturbance amplitude, measurement noise level, parameter convergence within the sliding window, and equivalent impedance change rate. If the confidence level of power grid strength identification is lower than the preset threshold, the network construction control parameters of the distribution network are determined to be conservative parameters. The formula for calculating the confidence level of power grid strength identification is as follows: in, To identify the confidence level for power grid strength, The effectiveness factor for the disturbance amplitude. To measure the noise factor, Let be the convergence factor of the parameter. The impedance change rate factor, , , , These are the weighting coefficients.

[0092] In one possible implementation, the instruction generation module 85 is also used for: After generating photovoltaic, energy storage, and charging power commands for the distribution network based on grid control parameters and constraints, the grid strength index set and comprehensive lifetime margin grid strength of the distribution network are updated based on the execution results of the photovoltaic, energy storage, and charging power commands. Based on the updated set of power grid strength indicators and the comprehensive life margin power grid strength, the network construction control parameters of the distribution network are updated.

[0093] This invention collects operating parameters of the distribution network, calculates the set of grid strength indicators and comprehensive lifetime margin, and constructs a two-dimensional operating partition based on grid strength and lifetime margin. This partition simultaneously reflects the support capacity of the external grid and the health status of internal equipment. Based on this, grid construction control parameters are adaptively determined according to different partition types, and power commands for photovoltaic, energy storage, and charging facilities are generated under various operating constraints. Voltage source-type grid construction control is executed, enabling the distribution network's photovoltaic-energy storage-charging system to dynamically adjust its grid support strength according to changes in grid strength. This solves the problem of mismatch between support strength and grid demand when fixed control parameters change grid conditions, reducing the risk of voltage exceedances and system oscillations. Simultaneously, equipment lifetime margin is incorporated into power allocation constraints. When grid support demand increases, power can be rationally allocated according to the remaining lifetime of each device, preventing accelerated aging of individual devices due to long-term high-intensity support tasks and extending the overall lifespan of the system. Through the synergy of grid strength perception and lifetime margin assessment, the distribution network's photovoltaic-energy storage-charging system can balance short-term support capacity and long-term operational reliability under varying operating conditions.

[0094] Figure 9 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 9 As shown, the electronic device 9 of this embodiment includes a processor 90 and a memory 91. The memory 91 stores a computer program 92. When the processor 90 executes the computer program 92, it implements the steps in the various method embodiments described above. Alternatively, when the processor 90 executes the computer program 92, it implements the functions of each module / unit in the various device embodiments described above.

[0095] For example, computer program 92 may be divided into one or more modules / units, which are stored in memory 91 and executed by processor 90 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 92 in electronic device 9.

[0096] Electronic device 9 may include, but is not limited to, processor 90 and memory 91. Those skilled in the art will understand that... Figure 9This is merely an example of electronic device 9 and does not constitute a limitation on electronic device 9. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 9 may also include input / output devices, network access devices, buses, etc.

[0097] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0098] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0099] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A control method for photovoltaic-storage-charging systems in a distribution network based on strength-margin, characterized in that, include: The set of power grid strength indicators for the distribution network is calculated based on the operating parameters of the distribution network. The device lifetime margin of each device is calculated based on the operating parameters of the distribution network, and the minimum value among the lifetime margins of each device is taken as the overall lifetime margin of the distribution network. Based on the power grid strength index set and the comprehensive lifetime margin, determine the two-dimensional operation zone type of the distribution network based on the power grid strength-life margin. A multi-objective optimization function is constructed based on the two-dimensional operation zoning type of power grid strength-life margin, and the network construction control parameters of the distribution network are determined. Based on the network control parameters and constraints, photovoltaic, energy storage, and charging power commands for the distribution network are generated.

2. The distribution network photovoltaic-storage-charging control method based on strength-margin according to claim 1, characterized in that, The calculation of the power grid strength index set of the distribution network based on the operating parameters of the distribution network includes: Extract the voltage, current and power changes at the grid connection point of the distribution network before and after the power disturbance, and estimate the equivalent grid impedance at the connection point; Calculate the short-circuit capacity and short-circuit ratio based on the equivalent grid impedance; Calculate the reactive power-voltage sensitivity and voltage stiffness indices based on the grid connection point voltage before and after the disturbance.

3. The distribution network photovoltaic-storage-charging control method based on strength-margin according to claim 1, characterized in that, The calculation of the device lifetime margin for each device based on the operating parameters of the distribution network includes: Calculate the power device losses based on the current, voltage, modulation ratio, and switching frequency of the photovoltaic inverter, energy storage converter, and charging converter. By inputting the power loss into the thermal network model, the junction temperature sequence of the power device is obtained; Online rainflow counting is performed on the junction temperature sequence of the power device to extract the thermal cycle amplitude, average temperature and number of cycles, thus obtaining a thermal cycle set; The cumulative lifetime damage of power devices is calculated based on the aforementioned thermal cycle set; The life margins of IGBTs, DC bus capacitors, and energy storage batteries are calculated based on the cumulative life damage of the power devices.

4. The distribution network photovoltaic-storage-charging control method based on strength-margin according to claim 1, characterized in that, The step of determining the two-dimensional operation zone type of the distribution network based on the power grid strength index set and the comprehensive lifetime margin includes: The power grid strength of the distribution network is determined based on the power grid strength index set. If the power grid strength is strong and the comprehensive lifetime margin is greater than or equal to the first margin threshold, then the power grid strength-life margin two-dimensional operation zoning type of the distribution network is determined to be a strong grid lifetime sufficient zone. If the power grid strength is medium and the comprehensive lifetime margin is greater than or equal to the second margin threshold, then the power grid strength-life margin two-dimensional operation zoning type of the distribution network is determined to be a medium power grid coordination zone; wherein, the second margin threshold is less than the first margin threshold; If the power grid strength is weak and the comprehensive lifetime margin is greater than or equal to the second margin threshold, then the power grid strength-life margin two-dimensional operation partition type of the distribution network is determined to be a weak network construction support area. If the power grid strength is strong or medium, and the comprehensive lifetime margin is less than the second margin threshold, then the power grid strength-life margin two-dimensional operation zoning type of the distribution network is determined to be a lifetime-limited protection zone. If the power grid strength is weak and the comprehensive lifetime margin is less than the second margin threshold, then the power grid strength-life margin two-dimensional operation partition type of the distribution network is determined to be a weak network lifetime-limited emergency support zone.

5. The distribution network photovoltaic-storage-charging control method based on strength-margin according to claim 1, characterized in that, The step of constructing a multi-objective optimization function based on the two-dimensional operation zoning type of power grid strength-life margin, and determining the network control parameters of the distribution network, includes: The weights of each optimization objective are determined based on the two-dimensional operation partition type of power grid strength-life margin and the preset weight mapping relationship. A multi-objective optimization function is constructed based on the weights of each optimization objective; the multi-objective optimization function is as follows: in, For the voltage deviation at the grid connection point, For frequency deviation, The increase in lifespan damage per unit time. The cost of solar power curtailment The cost of charging service deviations to These are the weighting coefficients.

6. The distribution network photovoltaic-storage-charging control method based on strength-margin according to claim 1, characterized in that, The step of constructing a multi-objective optimization function based on the two-dimensional operation zoning type of power grid strength-life margin, and determining the network construction control parameters of the distribution network, further includes: The grid strength identification confidence level of the distribution network is calculated based on the effectiveness of the disturbance amplitude, the measurement noise level, the convergence of parameters within the sliding window, and the rate of change of equivalent impedance. If the confidence level of the power grid strength identification is lower than the preset threshold, then the network construction control parameters of the distribution network are determined to be conservative parameters. The formula for calculating the confidence level of power grid strength identification is: in, To identify the confidence level for power grid strength, The effectiveness factor for the disturbance amplitude. To measure the noise factor, Let be the convergence factor of the parameter. The impedance change rate factor, , , , These are the weighting coefficients.

7. The distribution network photovoltaic-storage-charging control method based on intensity-margin according to any one of claims 1 to 6, characterized in that, After generating the photovoltaic, energy storage, and charging power commands for the distribution network based on the network control parameters and constraints, the method further includes: Based on the execution results of the photovoltaic, energy storage, and charging power commands, update the grid strength index set and comprehensive lifetime margin grid strength of the distribution network; The network control parameters of the distribution network are updated based on the updated set of power grid strength indicators and the comprehensive life margin power grid strength.

8. A power distribution network photovoltaic-storage-charging control device based on strength-margin, characterized in that, include: The strength calculation module is used to calculate the set of power grid strength indicators of the power distribution network based on the operating parameters of the power distribution network; The margin calculation module is used to calculate the device lifetime margin of each device based on the operating parameters of the distribution network, and take the minimum value of the lifetime margin of each device as the comprehensive lifetime margin of the distribution network. The partitioning determination module is used to determine the two-dimensional operation partitioning type of the distribution network based on the power grid strength index set and the comprehensive lifetime margin. The parameter determination module is used to construct a multi-objective optimization function based on the two-dimensional operation partition type of the power grid strength-life margin, and to determine the network construction control parameters of the distribution network. The instruction generation module is used to generate photovoltaic, energy storage and charging power instructions for the distribution network based on the network control parameters and constraints.

9. The distribution network photovoltaic-storage-charging control device based on strength-margin according to claim 8, characterized in that, The strength calculation module is specifically used for: Extract the voltage, current and power changes at the grid connection point of the distribution network before and after the power disturbance, and estimate the equivalent grid impedance at the connection point; Calculate the short-circuit capacity and short-circuit ratio based on the equivalent grid impedance; Calculate the reactive power-voltage sensitivity and voltage stiffness indices based on the grid connection point voltage before and after the disturbance.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

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