Solar street lamp power storage state sensing and intelligent compensation method and system

Abstract

This invention relates to the field of power supply and distribution technology, and in particular to a method and system for sensing and intelligently compensating the energy storage status of solar streetlights. It is applied to a solar streetlight system including an integrated photovoltaic-energy storage inverter, the system being connected to an AC power distribution network. The method includes the following steps: obtaining the battery's state of charge (SOC) and state of health (SOH), as well as the active power P and reactive power Q at the grid connection point; based on the SOC, SOH, and current active power P, this invention constructs a dynamic reactive capacity assessment model based on the battery's SOC and SOH. Unlike traditional schemes that only focus on inverter capacity limitations, this invention incorporates the battery's physiological health into the decision-making process. While tapping into idle capacity to participate in grid regulation, it avoids the risk of overload operation of aging batteries from the source, significantly extending equipment lifespan. Furthermore, the collaborative compensation mechanism breaks through the bottleneck of single-unit capacity, realizing the intensive utilization of regional resources.

Description

Technical Field

[0001] This invention relates to the field of power supply and distribution technology, and in particular to a method and system for sensing and intelligently compensating for the energy storage status of solar streetlights. Background Technology

[0002] With the rapid development of distributed new energy sources, integrated photovoltaic and energy storage streetlights have become a trend to participate in reactive power compensation in the distribution network as distributed energy storage nodes.

[0003] However, existing reactive power control strategies typically use only the inverter's apparent power limit as a boundary condition, ignoring the actual physiological state of the battery as an energy source. This single-dimensional decision-making mechanism often leads to the forced output of reactive power even when the battery is aging (low SOH) or low in charge (low SOC), easily inducing battery overload operation, accelerating equipment wear, and even causing safety accidents. In addition, traditional solutions are mostly limited to independent operation of a single unit. When faced with a large reactive power deficit, they are often unable to respond effectively due to the capacity bottleneck of a single unit, and lack a multi-unit collaborative mechanism within the region, resulting in low system resource utilization and increasing the overall cost of reactive power management in the distribution network.

[0004] Therefore, there is an urgent need for a reactive power compensation method that can comprehensively consider the battery health status and has regional coordination capabilities. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned shortcomings in the prior art by proposing a method and system for sensing and intelligently compensating the energy storage status of solar streetlights.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of this invention proposes a method for sensing and intelligently compensating the energy storage status of solar streetlights, applied to a solar streetlight system including a photovoltaic-energy storage integrated inverter, the system being connected to an AC power distribution network, comprising the following steps: Obtain the battery's state of charge (SOC) and state of health (SOH), as well as the active power (P) and reactive power (Q) at the grid connection point; Based on the SOC, SOH and the current active power P, dynamically evaluate the available reactive power capacity Q_available for the local inverter to participate in reactive power compensation; Based on the reactive power demand Q_ref of the grid connection point and the available reactive power capacity Q_available, determine whether the machine has the ability to independently complete reactive power compensation. When it is determined that the machine does not have the ability to complete the task independently, the reactive power compensation demand ΔQ that exceeds the machine's capacity is allocated to an external collaborative compensation unit for execution through the communication network.

[0007] Furthermore, the evaluation method for the available reactive power capacity Q_available includes: Based on the inverter's rated apparent capacity S_rated and the current active power P, calculate the theoretical reactive power upper limit Q_max_theory = ; The reactive power derating factor k is determined based on the SOC and SOH, where 0 ≤ k ≤ 1; Calculate Q_available = k × Q_max_theory.

[0008] Furthermore, the reactive power derating factor k is determined using a fuzzy logic control model: Using SOC and SOH as input variables and k as the output variable, a fuzzy rule table is preset. When SOC is below the first threshold or SOH is below the second threshold, k approaches 0; When SOC is higher than the third threshold and SOH is higher than the fourth threshold, k approaches 1; When SOC and SOH are in an intermediate state, k is between 0.4 and 0.6.

[0009] Furthermore, determining whether the machine has the ability to independently perform reactive power compensation includes: Based on the power factor assessment target of the distribution network and the current reactive power Q, generate reactive power adjustment command Q_ref; Calculate the estimated active power loss P_loss generated inside the inverter when Q_ref is executed; Verify whether the current battery meets the total active power output constraint: P + P_loss ≤ P_bat_available, where P_bat_available is the current maximum allowable discharge power of the battery; If the verification is satisfied and |Q_ref| ≤ Q_available, then it is determined that it has the ability to complete the task independently, and the local inverter can independently undertake all reactive power regulation tasks. Otherwise, it is determined that the individual does not have the ability to complete the task independently, and the excess portion ΔQ = |Q_ref| - Q_available is calculated.

[0010] Furthermore, the external collaborative compensation unit includes: Photovoltaic and energy storage integrated inverter for adjacent solar streetlights located in the same geographical area as the current streetlights and interconnected via a communication network; Alternatively, a centralized reactive power compensation device deployed at the end of a power distribution line, including capacitor banks, reactor banks, or static var generators (SVG).

[0011] Furthermore, when allocating ΔQ to the photovoltaic-storage integrated inverter of adjacent solar streetlights, the method further includes: Obtain the current active power P_i, state of charge SOC_i, and health status SOH_i of the adjacent street light inverters; Based on the SOC_i, SOH_i and P_i of each adjacent street light, calculate the available reactive capacity Q_available_i of the inverter of each adjacent street light; With the goal of maximizing the equalization of the remaining reactive power margin of each adjacent street light, a consensus algorithm or droop control is used to distribute ΔQ to each adjacent street light inverter for execution.

[0012] Furthermore, obtaining the battery's state of charge (SOC) and state of health (SOH) specifically includes: obtaining the SOC and SOH through a composite algorithm combining the extended Kalman filter (EKF) and the open-circuit voltage method. During the battery's resting period, the initial SOC value and full charge capacity are corrected by directly looking up the table using the open circuit voltage method, which is used to update the SOH. During the dynamic operation phase, the Extended Kalman Filter (EKF) is used to fuse the ampere-hour integral method with the voltage response model to dynamically track the State of Charge (SOC) in real time.

[0013] Furthermore, it also includes an adaptive switching step for operating modes: Real-time monitoring of the AC power distribution network status to determine whether it is currently in grid-connected or off-grid mode; If it is determined to be off-grid mode, the reactive power compensation function is blocked, and the local load management strategy based on SOC and SOH is switched. If it is determined that the grid-connected mode has been restored, the reactive power compensation function will be reactivated.

[0014] A second aspect of the present invention also proposes a solar street light energy storage status sensing and intelligent compensation system for implementing the above-mentioned method, the system comprising: The acquisition unit acquires the battery's state of charge (SOC) and state of health (SOH), as well as the active power (P) and reactive power (Q) at the grid connection point. The evaluation unit dynamically evaluates the available reactive power capacity Q_available of the local inverter for reactive power compensation based on the SOC, SOH and the current active power P. The judgment unit determines whether the machine has the ability to independently complete reactive power compensation based on the reactive power demand Q_ref of the grid connection point and the available reactive power capacity Q_available. When the allocation unit determines that it does not have the ability to complete the task independently, it allocates the reactive power compensation demand ΔQ that exceeds the local unit's capacity to an external collaborative compensation unit for execution through the communication network.

[0015] Furthermore, it also includes an adaptive switching unit for operating modes: Real-time monitoring of the AC power distribution network status to determine whether it is currently in grid-connected or off-grid mode; If it is determined to be off-grid mode, the reactive power compensation function is blocked, and the local load management strategy based on SOC and SOH is switched. If it is determined that the grid-connected mode has been restored, the reactive power compensation function will be reactivated.

[0016] The present invention proposes a method and system for sensing and intelligently compensating the energy storage status of solar streetlights. The beneficial effects are as follows: This invention constructs a dynamic reactive power capacity assessment model based on the battery's State of Charge (SOC) and State of Health (SOH). Unlike traditional solutions that only focus on inverter capacity limitations, this invention incorporates the battery's physiological health into the decision-making process. While tapping into idle capacity to participate in grid regulation, it avoids the risk of overload operation of aging batteries from the source, significantly extending equipment lifespan. Furthermore, the collaborative compensation mechanism breaks through the bottleneck of single-unit capacity, realizing intensive utilization of regional resources and greatly reducing the reactive power management cost of the distribution network. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for sensing and intelligently compensating the energy storage status of a solar street light according to the present invention. Figure 2 This is a flowchart illustrating another method for sensing and intelligently compensating the energy storage status of solar streetlights according to the present invention. Figure 3 This is a schematic diagram of a solar street light energy storage status sensing and intelligent compensation system according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that the terms "first," "second," "third," "fourth," etc., used in the specification and claims of this invention are used to distinguish different objects, not to describe a specific order. The terms "comprising" and "having," and any variations thereof, in the embodiments of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0020] Example 1 Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for sensing and intelligently compensating the energy storage status of a solar street light, as disclosed in an embodiment of the present invention. It is applied to a solar street light system including an integrated photovoltaic-energy storage inverter, the system being connected to an AC power distribution network, and specifically includes the following steps: 101. Obtain the battery's state of charge (SOC) and state of health (SOH), as well as the active power (P) and reactive power (Q) at the grid connection point; As an optional embodiment of the present invention, the acquisition of the battery's State of Charge (SOC) and State of Health (SOH) specifically includes: the SOC and SOH are obtained through a composite algorithm combining Extended Kalman Filter (EKF) and Open-Circuit Voltage Method (OCPS). During the battery's resting period, the OC initial value and full-charge capacity are directly corrected by looking up a table using the OCPS method to update the SOH. During dynamic operation, the EKF is used to fuse the ampere-hour integral method and voltage response model to dynamically track the SOC in real time. The grid status perception is achieved by configuring sensors at the inverter grid connection point and using FFT or DQ transform technology to decompose and calculate active power P and reactive power Q in real time, sensing the energy interaction status between the system and the grid. By constructing a source-grid dual-side holographic perception and introducing the SOH parameter, the shortcomings of traditional control that ignores battery aging are compensated for, providing a multi-dimensional decision-making basis including battery health for subsequent strategies, and effectively avoiding the risk of overload operation of aging batteries.

[0021] 102. Based on the SOC, SOH and the current active power P, dynamically evaluate the available reactive power capacity Q_available for the local inverter to participate in reactive power compensation; As an optional implementation of the present invention, a safe power envelope that is dynamically adjusted according to the battery status is established, which solves the problem that the traditional solution only considers the inverter capacity and ignores the actual load-carrying capacity of the power supply battery. While maximizing the use of idle capacity for grid regulation, a battery safety protection wall is constructed, which significantly extends the equipment life.

[0022] 103. Based on the reactive power demand Q_ref of the grid connection point and the available reactive power capacity Q_available, determine whether the machine has the ability to independently complete reactive power compensation; As an optional embodiment of the present invention, reactive power demand instructions are calculated based on the power factor assessment target of the distribution network. ,Will and The comparison is performed, and the internal active power loss generated by the executed instructions is pre-calculated. The maximum power constraint of the battery is comprehensively verified. If both the capacity and power constraints are met, the battery is deemed to have the ability to complete the task independently; otherwise, it is deemed not to have the ability.

[0023] In this embodiment, a pre-verification approach of evaluation before execution is adopted. By introducing loss prediction and multiple constraint judgments, the risk of inverter overload or battery over-discharge caused by blindly responding to commands is avoided, ensuring the reliability of compensation actions and the stability of system operation.

[0024] 104. When it is determined that the machine does not have the ability to complete the task independently, the reactive power compensation demand ΔQ that exceeds the machine's capacity is allocated to an external collaborative compensation unit for execution through the communication network.

[0025] As an optional embodiment of the present invention, the reactive power deficit is calculated. By utilizing communication networking modules such as ZigBee, LoRa, or power line carrier, the shortage information is broadcast to adjacent street light nodes or centralized compensation devices in the same area. External coordinating units respond to the request based on their own idle capacity and assist in completing the remaining compensation task.

[0026] In this embodiment, the isolated effect of single-unit street light control is broken. Through the cluster collaboration mechanism, the intensive use of regional power facility resources is realized. This not only improves the success rate and coverage of reactive power compensation in the distribution network, but also avoids blindly increasing hardware costs to cope with extreme single-point operating conditions, thus achieving the optimal cost-effectiveness at the system level.

[0027] In summary, this invention constructs a dynamic reactive power capacity assessment model based on the battery's State of Charge (SOC) and State of Health (SOH). Unlike traditional solutions that only focus on inverter capacity limitations, this invention incorporates the battery's physiological health into the decision-making process. While tapping into idle capacity to participate in grid regulation, it avoids the risk of overload operation of aging batteries from the source, significantly extending equipment lifespan. Furthermore, the collaborative compensation mechanism breaks through the bottleneck of single-unit capacity, realizing the intensive utilization of regional resources and greatly reducing the reactive power management cost of the distribution network.

[0028] Example 2 Please refer to Figure 2 , Figure 2 This is a flowchart illustrating another method for sensing and intelligently compensating the energy storage status of solar streetlights disclosed in an embodiment of the present invention. Figure 2 As shown, the method for sensing and intelligently compensating the energy storage status of a solar street light may include the following steps: 201. Obtain the battery's state of charge (SOC) and state of health (SOH), as well as the active power (P) and reactive power (Q) at the grid connection point; As an optional embodiment of the present invention, the acquisition of the battery's State of Charge (SOC) and State of Health (SOH) specifically includes: the SOC and SOH are obtained through a composite algorithm combining Extended Kalman Filter (EKF) and Open-Circuit Voltage Method (OCPS). During the battery's resting period, the OC initial value and full-charge capacity are directly corrected by looking up a table using the OCPS method to update the SOH. During dynamic operation, the EKF is used to fuse the ampere-hour integral method and the voltage response model to dynamically track the SOC in real time. The grid status perception is achieved by configuring sensors at the inverter grid connection point and using FFT or DQ transform technology to decompose and calculate active power P and reactive power Q in real time, accurately sensing the energy interaction status between the system and the grid, constructing a holographic perception on both the source and grid sides. By introducing the SOH parameter, the shortcomings of traditional control that ignores battery aging are compensated for, providing a multi-dimensional decision-making basis including battery health for subsequent strategies, and effectively avoiding the risk of overload operation of aging batteries.

[0029] Based on the SOC, SOH, and current active power P, dynamically assess the available reactive power capacity Q_available for reactive power compensation of the local inverter, which specifically includes: 202. Based on the inverter's rated apparent capacity S_rated and the current active power P, calculate the theoretical upper limit of reactive power Q_max_theory = ; 203. Determine the reactive power derating factor k based on the SOC and SOH, where 0 ≤ k ≤ 1; 204. Calculate Q_available = k × Q_max_theory.

[0030] In an optional embodiment, the reactive power derating factor k in this invention is determined by a fuzzy logic control model: Using SOC and SOH as input variables and k as the output variable, a fuzzy rule table is preset. When SOC is below the first threshold or SOH is below the second threshold, k approaches 0; When SOC is higher than the third threshold and SOH is higher than the fourth threshold, k approaches 1; When SOC and SOH are in an intermediate state, k is between 0.4 and 0.6.

[0031] Specifically, this embodiment employs a strategy combining physical limit calculations and battery state correction to evaluate available reactive power capacity. The specific steps are as follows: First, the system establishes the physical limits of the inverter based on the power triangle principle, and then determines the rated apparent capacity of the inverter. Compared with current active power Calculate the theoretical upper limit of reactive power, which is only subject to hardware constraints: This value represents the maximum reactive power that the inverter can theoretically output under the current active power conditions.

[0032] Subsequently, a fuzzy logic control model is introduced to perform state correction on the above theoretical values ​​in order to determine the reactive power derating factor. ( The model uses the battery's state of charge (SOC) and state of health (SOH) as input variables. For the output variables, logical decisions are made using a pre-defined fuzzy rule table: Protection priority strategy: When the SOC is lower than the first threshold (low charge) or the SOH is lower than the second threshold (severe aging), the battery's load-carrying capacity is determined to be weak, and the control output is adjusted accordingly. Approaching zero, reactive power output is greatly reduced, prioritizing lighting load and battery safety; Performance-priority strategy: When the SOC is higher than the third threshold and the SOH is higher than the fourth threshold, the battery is considered healthy and has sufficient energy. At this time, the output is controlled. Approaching 1, it allows inverters to fully utilize physical capacity to participate in grid regulation; Equilibrium strategy: When SOC and SOH are in an intermediate state, control Between 0.4 and 0.6, a moderate safety margin is maintained.

[0033] Finally, by combining the theoretical limit with the state correction factor, the actual available reactive power capacity is calculated: Such as the rated apparent capacity of a photovoltaic-storage integrated inverter The active power consumed by the current street lighting load .

[0034] Based on step 202, calculate the theoretical upper limit of reactive power: This is the maximum reactive power output capability allowed by the inverter hardware.

[0035] Next, based on the fuzzy logic rule table, the system will execute different processing strategies for different battery states: If the battery is in good condition, full output mode. For example, the current state of charge of the battery (Above the third threshold 80%), healthy status (If the battery level is above the fourth threshold of 80%), the fuzzy controller determines that the battery is "fully charged and healthy", triggers the performance-priority strategy, and outputs the derating factor. Approaching 1 (e.g.) ), .

[0036] The system determines that the battery is capable of handling the additional load, so it operates at full power, making full use of the inverter's idle capacity to provide maximum reactive power support to the grid.

[0037] In this embodiment, on the one hand, fuzzy logic is used to achieve flexible derating of reactive power output, which overcomes the critical oscillation problem that is easily caused by traditional hard threshold control and ensures the smooth and stable adjustment process; on the other hand, the battery health status is mapped to control constraints, realizing the dual matching of physical capacity and load capacity, effectively avoiding the hidden overload risk of aging batteries, and significantly extending the service life of the energy storage system.

[0038] Based on the reactive power demand Q_ref of the grid connection point and the available reactive power capacity Q_available, determine whether the machine has the ability to independently complete reactive power compensation, which specifically includes: 205. Based on the power factor assessment target of the distribution network and the current reactive power Q, generate a reactive power adjustment command Q_ref; 206. Calculate the estimated active power loss P_loss generated inside the inverter when executing Q_ref; 207. Verify whether the current battery meets the total active power output constraint: P + P_loss ≤ P_bat_available, where P_bat_available is the maximum allowable discharge power of the battery at present; 208. If the verification is satisfied and |Q_ref| ≤ Q_available, then it is determined that it has the ability to complete the task independently, and the local inverter independently undertakes all reactive power regulation tasks. 209. Otherwise, it is determined that the individual does not have the ability to complete the task independently, and the excess portion ΔQ = |Q_ref| - Q_available is calculated.

[0039] The calculation model for the estimated active power loss P_loss is as follows: P_loss = a·|Q_ref| 2 + b·|Q_ref| + c, Where a, b, and c are inverter loss coefficients obtained in advance through experimental calibration or online identification, corresponding to the inverter's copper loss, switching loss, and fixed loss, respectively.

[0040] In an optional embodiment, the system of the present invention generates the required reactive power regulation command based on the power factor assessment target of the distribution network and the reactive power Q at the current grid connection point. .

[0041] Subsequently, a crucial active power loss prediction step is introduced. Before formally executing the command, the system uses a preset loss model to calculate the inverter output. The internal active power loss that will occur at that time: in, , , These are the characteristic coefficients corresponding to copper loss, switching loss, and fixed loss, respectively. This model quantifies the implicit energy cost in the reactive power transmission process.

[0042] Verify total active power demand, i.e., lighting load. Compared with the estimated loss Does the sum exceed the battery's current maximum allowable discharge power? That is, verification Is this valid? This measure aims to prevent battery overload. Verify the absolute value of reactive power demand command Is it within the available reactive power capacity of this machine? Within its coverage area.

[0043] The unit is deemed capable of independent operation only if both the power and capacity constraints are met simultaneously, and the inverter independently undertakes the task. If either constraint is not met, the unit is deemed not capable of independent operation, and the reactive power deficit is immediately calculated. This will provide data support for the subsequent launch of a collaborative compensation mechanism.

[0044] For example, assume the active load of streetlights The battery's current maximum allowable discharge power This machine can utilize reactive power capacity Inverter loss factor: (Loss model:) ); If the power grid issues a reactive power regulation command .

[0045] The system calculates the internal overhead incurred in executing this task: Total power required = Lamp load + Loss .because (Battery maximum power) Power verification passed, requirements (Available capacity), capacity verification passed.

[0046] Therefore, the system determines that the unit has the capability to complete the task independently, and the inverter on the unit will bear the full responsibility. The compensation task.

[0047] Specifically, this embodiment introduces a loss prediction model to reveal the actual energy consumption of the battery by reactive power compensation, solving the problem of battery energy budget deviation caused by neglecting losses in traditional solutions. On the other hand, it establishes a dual verification mechanism for power and capacity, ensuring that the system only acts independently when the battery can power it and the inverter can generate power. This fundamentally eliminates the risk of battery over-discharge or inverter overload caused by blindly responding to grid commands, and significantly improves the operational reliability of the system.

[0048] 210. When it is determined that the machine does not have the capability to complete the task independently, the reactive power compensation demand ΔQ exceeding the machine's capability is allocated to an external collaborative compensation unit for execution via the communication network. The external collaborative compensation unit includes: Photovoltaic and energy storage integrated inverter for adjacent solar streetlights located in the same geographical area as the current streetlights and interconnected via a communication network; Alternatively, a centralized reactive power compensation device deployed at the end of a power distribution line, including capacitor banks, reactor banks, or static var generators (SVG).

[0049] Specifically, it includes: When allocating ΔQ to the photovoltaic-storage integrated inverter of adjacent solar streetlights, it further includes: 211. Obtain the current active power P_i, state of charge SOC_i, and health status SOH_i of the adjacent street light inverters; 212. Based on the SOC_i, SOH_i and P_i of each adjacent street light, calculate the available reactive power capacity Q_available_i of each adjacent street light inverter. 213. With the goal of maximizing the equalization of the remaining reactive power margin of each adjacent street light, a consensus algorithm or droop control is used to distribute ΔQ to each adjacent street light inverter for execution.

[0050] Specifically, in this embodiment, when the local machine determines that it cannot independently meet the reactive power demand, the system immediately transmits the deficit through the communication network. The data is then distributed to external collaborative compensation units. These units prioritize calling upon the photovoltaic-storage integrated inverters of adjacent solar streetlights interconnected within the same geographical area to utilize their idle capacity for neighborhood mutual assistance. When distributed resources are insufficient, they coordinate with capacitor banks, reactors, or static var generators (SVG) deployed at the end of the power distribution lines as backup supplements.

[0051] Secondly, for the high-priority neighboring street light mutual assistance mode, a state-aware optimization allocation strategy is adopted. The real-time status of neighboring street lights is polled through the communication network to obtain the current active power of each neighboring inverter. State of charge and health status Each adjacent street light reuses the capacity assessment logic of this invention, based on its own... , and Calculate their respective available reactive power capacity. Each should clearly define its own maximum support limit.

[0052] This implementation introduces a neighborhood mutual assistance mechanism to virtualize isolated energy storage units into a mobile energy storage power station. Without increasing hardware costs, it uses software algorithms to tap into idle power resources in the area, effectively solving the bottleneck of insufficient single-unit capacity. Furthermore, based on a margin-balanced allocation algorithm, it ensures the reasonable sharing of collaborative tasks, extends the service life of the entire street light cluster, and realizes the intensive utilization of resources in the area.

[0053] 214. It also includes the step of adaptive switching of operating modes: 2141. Real-time monitoring of the AC power distribution network status to determine whether it is currently in grid-connected or off-grid mode; 2142. If it is determined to be off-grid mode, the reactive power compensation function is locked and the local load management strategy based on SOC and SOH is switched. The local load management strategy includes: dynamically adjusting the power consumption of street lighting load according to the current battery state of charge (SOC) and state of health (SOH) to extend the battery power supply time. 2143. If it is determined that the grid-connected mode has been restored, the reactive power compensation function will be reactivated.

[0054] In other words, in this embodiment, the system monitors electrical characteristics such as voltage and frequency at the grid connection point in real time to identify whether it is currently in a grid-connected or off-grid state. When it is determined that it has entered off-grid mode, the system immediately locks the reactive power compensation function to prevent the risk of islanding operation due to reactive power injection; at the same time, it switches the control from grid service to local protection, and implements local load management based on the battery SOC and SOH status, such as dynamically adjusting lighting power to extend emergency power supply time. Once the grid is detected to be restored, the system automatically unlocks and reactivates the reactive power compensation function.

[0055] This design establishes a reliable safety barrier, which not only avoids compliance risks of off-grid operation but also ensures the core lighting function in emergency situations, achieving an intelligent balance between serving the power grid and protecting people's livelihoods.

[0056] Example 3 Please refer to Figure 3 , Figure 3 This is a structural block diagram of a solar street light energy storage status sensing and intelligent compensation system disclosed in an embodiment of the present invention. Figure 3 As shown, a solar street light energy storage status sensing and intelligent compensation system, used to implement the above method, includes: The acquisition unit 301 acquires the battery's state of charge (SOC) and state of health (SOH), as well as the active power P and reactive power Q at the grid connection point. As an optional embodiment of the present invention, the acquisition of the battery's State of Charge (SOC) and State of Health (SOH) specifically includes: the SOC and SOH are obtained through a composite algorithm combining Extended Kalman Filter (EKF) and Open-Circuit Voltage Method (OCPS). During the battery's resting period, the OC initial value and full-charge capacity are directly corrected by looking up a table using the OCPS method to update the SOH. During dynamic operation, the EKF is used to fuse the ampere-hour integral method and voltage response model to dynamically track the SOC in real time. The grid status perception is achieved by configuring sensors at the inverter grid connection point and using FFT or DQ transform technology to decompose and calculate active power P and reactive power Q in real time, sensing the energy interaction status between the system and the grid. By constructing a source-grid dual-side holographic perception and introducing the SOH parameter, the shortcomings of traditional control that ignores battery aging are compensated for, providing a multi-dimensional decision-making basis including battery health for subsequent strategies, and effectively avoiding the risk of overload operation of aging batteries.

[0057] Evaluation unit 302 dynamically evaluates the available reactive power capacity Q_available of the local inverter for reactive power compensation based on the SOC, SOH and the current active power P; As an optional implementation of the present invention, a safe power envelope that is dynamically adjusted according to the battery status is established, which solves the problem that the traditional solution only considers the inverter capacity and ignores the actual load-carrying capacity of the power supply battery. While maximizing the use of idle capacity for grid regulation, a battery safety protection wall is constructed, which significantly extends the equipment life.

[0058] The judgment unit 303 determines whether the machine has the ability to independently complete reactive power compensation based on the reactive power demand Q_ref of the grid connection point and the available reactive power capacity Q_available. As an optional embodiment of the present invention, reactive power demand instructions are calculated based on the power factor assessment target of the distribution network. ,Will and The comparison is performed, and the internal active power loss generated by the executed instructions is pre-calculated. The maximum power constraint of the battery is comprehensively verified. If both the capacity and power constraints are met, the battery is deemed to have the ability to complete the task independently; otherwise, it is deemed not to have the ability.

[0059] In this embodiment, a pre-verification method of evaluation before execution is adopted. By introducing loss prediction and multiple constraint judgment, the risk of inverter overload or battery over-discharge caused by blindly responding to commands is avoided, ensuring the reliability of compensation action and the stability of system operation.

[0060] When the allocation unit 304 determines that the local machine does not have the ability to complete the task independently, it allocates the reactive power compensation demand ΔQ that exceeds the local machine's capacity to an external collaborative compensation unit for execution through the communication network.

[0061] As an optional embodiment of the present invention, the reactive power deficit is calculated. By utilizing communication networking modules such as ZigBee, LoRa, or power line carrier, the shortage information is broadcast to adjacent street light nodes or centralized compensation devices in the same area. External coordinating units respond to the request based on their own idle capacity and assist in completing the remaining compensation task.

[0062] This embodiment breaks the island effect of single-unit street light control. Through a cluster collaboration mechanism, it achieves intensive utilization of regional power facility resources, which not only improves the success rate and coverage of reactive power compensation in the distribution network, but also avoids blindly increasing hardware costs to cope with extreme single-point operating conditions, thus achieving optimal cost-effectiveness at the system level.

[0063] Based on the above embodiments, the present invention further includes an adaptive operation mode switching unit 305: Real-time monitoring of the AC power distribution network status to determine whether it is currently in grid-connected or off-grid mode; If it is determined to be off-grid mode, the reactive power compensation function is blocked, and the local load management strategy based on SOC and SOH is switched. If it is determined that the grid-connected mode has been restored, the reactive power compensation function will be reactivated.

[0064] In other words, in this embodiment, the system monitors electrical characteristics such as voltage and frequency at the grid connection point in real time to identify whether it is currently in a grid-connected or off-grid state. When it is determined that it has entered off-grid mode, the system immediately locks the reactive power compensation function to prevent the risk of islanding operation due to reactive power injection; at the same time, it switches the control from grid service to local protection, and implements local load management based on the battery SOC and SOH status, such as dynamically adjusting lighting power to extend emergency power supply time. Once the grid is detected to be restored, the system automatically unlocks and reactivates the reactive power compensation function.

[0065] This design establishes a reliable safety barrier, which not only avoids compliance risks of off-grid operation but also ensures the core lighting function in emergency situations, achieving an intelligent balance between serving the power grid and protecting people's livelihoods.

[0066] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for sensing and intelligently compensating the energy storage status of a solar street light, applied to a solar street light system including a photovoltaic-energy storage integrated inverter, wherein the system is connected to an AC power distribution network, characterized in that... Includes the following steps: Obtain the battery's state of charge (SOC) and state of health (SOH), as well as the active power (P) and reactive power (Q) at the grid connection point; Based on the SOC, SOH and the current active power P, dynamically evaluate the available reactive power capacity Q_available for the local inverter to participate in reactive power compensation; Based on the reactive power demand Q_ref of the grid connection point and the available reactive power capacity Q_available, determine whether the machine has the ability to independently complete reactive power compensation. When it is determined that the machine does not have the ability to complete the task independently, the reactive power compensation demand ΔQ that exceeds the machine's capacity is allocated to an external collaborative compensation unit for execution through the communication network.

2. The method for sensing and intelligently compensating the energy storage status of a solar street light according to claim 1, characterized in that: The evaluation methods for the available reactive power capacity Q_available include: Based on the inverter's rated apparent capacity S_rated and the current active power P, calculate the theoretical upper limit of reactive power Q_max_theory= ; The reactive power derating factor k is determined based on the SOC and SOH, where 0 ≤ k ≤ 1; Calculate Q_available = k × Q_max_theory.

3. The method for sensing and intelligently compensating the energy storage status of a solar street light according to claim 2, characterized in that: The reactive power derating factor k is determined through a fuzzy logic control model: Using SOC and SOH as input variables and k as the output variable, a fuzzy rule table is preset. When SOC is below the first threshold or SOH is below the second threshold, k approaches 0; When SOC is higher than the third threshold and SOH is higher than the fourth threshold, k approaches 1; When SOC and SOH are in an intermediate state, k is between 0.4 and 0.

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4. The method for sensing and intelligently compensating the energy storage status of a solar street light according to claim 1, characterized in that: The determination of whether the machine has the ability to independently complete reactive power compensation includes: Based on the power factor assessment target of the distribution network and the current reactive power Q, generate reactive power adjustment command Q_ref; Calculate the estimated active power loss P_loss generated inside the inverter when Q_ref is executed; Verify whether the current battery meets the total active power output constraint: P + P_loss ≤ P_bat_available, where P_bat_available is the current maximum allowable discharge power of the battery; If the verification is satisfied and |Q_ref| ≤ Q_available, then it is determined that it has the ability to complete the task independently, and the local inverter independently undertakes all reactive power regulation tasks. Otherwise, it is determined that the individual does not have the ability to complete the task independently, and the excess portion ΔQ = |Q_ref| - Q_available is calculated.

5. The method for sensing and intelligently compensating the energy storage status of a solar street light according to claim 1, characterized in that: The external collaborative compensation unit includes: Photovoltaic and energy storage integrated inverter for adjacent solar streetlights located in the same geographical area as the current streetlights and interconnected via a communication network; Alternatively, a centralized reactive power compensation device deployed at the end of a power distribution line, including capacitor banks, reactor banks, or static var generators (SVG).

6. The method for sensing and intelligently compensating the energy storage status of a solar street light according to claim 5, characterized in that: When allocating ΔQ to the photovoltaic-storage integrated inverter of adjacent solar streetlights, it further includes: Obtain the current active power P_i, state of charge SOC_i, and health status SOH_i of the adjacent street light inverters; Based on the SOC_i, SOH_i and P_i of each adjacent street light, calculate the available reactive capacity Q_available_i of the inverter of each adjacent street light; With the goal of maximizing the equalization of the remaining reactive power margin of each adjacent street light, a consensus algorithm or droop control is used to distribute ΔQ to each adjacent street light inverter for execution.

7. The method for sensing and intelligently compensating the energy storage status of a solar street light according to claim 1, characterized in that: The acquisition of the battery's state of charge (SOC) and state of health (SOH) specifically includes: obtaining the SOC and SOH using a composite algorithm combining the extended Kalman filter (EKF) and the open-circuit voltage method. During the battery resting period, the open-circuit voltage method is used to directly look up the table to correct the initial SOC value and full charge capacity, which is used to update the SOH. During the dynamic operation phase, the Extended Kalman Filter (EKF) is used to fuse the ampere-hour integral method with the voltage response model to dynamically track the State of Charge (SOC) in real time.

8. The method for sensing and intelligently compensating the energy storage status of a solar street light according to claim 1, characterized in that: It also includes the step of adaptive switching of operating modes: Real-time monitoring of the AC power distribution network status to determine whether it is currently in grid-connected or off-grid mode; If it is determined to be off-grid mode, the reactive power compensation function is blocked, and the local load management strategy based on SOC and SOH is switched. If it is determined that the grid-connected mode has been restored, the reactive power compensation function will be reactivated.

9. A solar street light energy storage status sensing and intelligent compensation system, used to implement the method described in any one of claims 1-8, characterized in that, include: The acquisition unit acquires the battery's state of charge (SOC) and state of health (SOH), as well as the active power (P) and reactive power (Q) at the grid connection point. The evaluation unit dynamically evaluates the available reactive power capacity Q_available of the local inverter for reactive power compensation based on the SOC, SOH and the current active power P. The judgment unit determines whether the machine has the ability to independently complete reactive power compensation based on the reactive power demand Q_ref of the grid connection point and the available reactive power capacity Q_available. When the allocation unit determines that it does not have the ability to complete the task independently, it allocates the reactive power compensation demand ΔQ that exceeds the local unit's capacity to an external collaborative compensation unit for execution through the communication network.

10. A solar street light energy storage status sensing and intelligent compensation system according to claim 9, characterized in that: It also includes an adaptive switching unit for operating modes: Real-time monitoring of the AC power distribution network status to determine whether it is currently in grid-connected or off-grid mode; If it is determined to be off-grid mode, the reactive power compensation function is blocked, and the local load management strategy based on SOC and SOH is switched. If it is determined that the grid-connected mode has been restored, the reactive power compensation function will be reactivated.

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