Method for optimizing overload capacity of low-voltage networking type energy storage equipment and related equipment

Abstract

The invention discloses a method for optimizing the overload capacity of low-voltage networking type energy storage equipment and related equipment, and belongs to the technical field of electric energy storage. The method comprises the following steps: collecting short-circuit current and voltage data of a power system in real time, and calculating the current short-circuit capacity according to the short-circuit current and voltage data; the output power of the energy storage equipment is automatically adjusted according to the change of the short-circuit capacity; and when the short-circuit capacity exceeds the set threshold value, the output of the energy storage equipment is cut off, and the cooling system is started. The invention further provides a corresponding optimization module and system, electronic equipment, a computer readable storage medium and a computer program product. According to the method, the short-circuit capacity is monitored in real time, the output power of the energy storage equipment is dynamically adjusted, the maximum allowable output power calculation method based on the safety coefficient is adopted, dynamic adjustment is conducted in combination with the equipment temperature and the environment humidity, the overload capacity and operation safety of the low-voltage networking type energy storage equipment are effectively improved, and the service life of the equipment is prolonged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power energy storage, in particular to a low-voltage grid-forming energy storage device overload capacity optimization method and related equipment. BACKGROUND

[0002] With the large-scale access of new energy power generation, the stability of the power system is facing new challenges. As an important power system stability support device, the optimization of the overload capacity of the grid-forming energy storage device is of great significance to ensure the safe and stable operation of the power grid.

[0003] With the increasing proportion of renewable energy in the power system, low-voltage grid-forming energy storage devices are increasingly widely used in the power system. Low-voltage grid-forming energy storage devices are low-voltage energy storage systems that use grid-forming technology. The core breakthrough is to simulate the operating characteristics of synchronous generators through power electronic converters, enabling the energy storage system to autonomously build voltage and frequency. Unlike traditional grid-following energy storage, which relies on grid voltage phase synchronization, grid-forming energy storage can operate stably in grid-connected or off-grid states, especially in weak grid or island scenarios. Its technical implementation relies on advanced control algorithms for energy storage converters (PCS), such as virtual synchronous generator (VSG) technology. This technology simulates the rotor inertia, damping characteristics, and excitation regulation process of synchronous generators, enabling the energy storage system to have millisecond-level frequency response, second-level power adjustment capability, and provide reactive power support and short-circuit capacity enhancement functions.

[0004] Energy storage devices in the power system are not only used for peak shaving, frequency and voltage regulation, but also provide emergency backup power during system faults. However, the short-circuit capacity in the power system varies greatly, which puts higher requirements on the overload capacity of energy storage devices.

[0005] Currently, the configuration and operation of grid-forming energy storage devices mainly focus on capacity optimization and location selection. For example, some research has proposed an optimization configuration method for grid-forming energy storage devices based on transient overvoltage safety constraints. By collecting transient overvoltage data of each new energy station under AC and DC fault scenarios, safety constraints are established to improve the voltage support strength of the sending end of the power grid with the smallest configuration capacity (CN118797893A). Another study considers the impact of climate change on grid-forming energy storage systems and proposes a grid-forming energy storage capacity optimization device and method. Through data acquisition, photovoltaic power generation prediction, and consumption capacity calculation modules, the accuracy of grid-forming energy storage capacity optimization is improved (CN119315590A).

[0006] In terms of short-circuit current measurement, a method for determining the short-circuit current of a network-forming energy storage system device is proposed. This method obtains the pre-fault data of the target power grid, calculates the inverter capacity and operating mode transfer coefficient, and then calculates the short-circuit current of the energy storage system and the short-circuit current of the target power grid after short-circuit, finally determines the short-circuit current of the network-forming energy storage system device (CN119438973A). In addition, research has focused on the capacity configuration optimization problem of network-forming power supply, and proposed a network-forming power supply capacity configuration optimization method based on new energy grid-connected stability constraints. By verifying the short-circuit ratio constraint, static security constraint and transient stability constraint, the optimal access location and capacity of network-forming power supply when new energy single station is connected to the grid are determined (CN119401573A).

[0007] It is particularly noteworthy that research has considered the optimization configuration problem of network-forming energy storage under short-circuit ratio constraint, by constructing a double-layer optimization configuration model, constraining construction cost and operation and maintenance cost in the upper model, considering normal optimization scheduling and fault recovery in the lower model, and setting short-circuit ratio constraint, the optimal solution is obtained by particle swarm algorithm (CN118657041A).

[0008] However, the existing technology has the following shortcomings: first, the existing energy storage devices often do not fully consider the overload demand under different short-circuit capacities when designing, which leads to problems such as device overload damage or system stability decline in actual operation; second, the existing technology mainly focuses on the capacity configuration and charge-discharge control strategy of energy storage devices, lacking systematic research on the overload capacity of energy storage devices under different short-circuit capacities; third, the existing technology does not establish a dynamic adjustment mechanism between short-circuit capacity and energy storage device output power, which cannot automatically optimize the overload capacity of energy storage devices according to the real-time changes of power grid short-circuit capacity; finally, the existing technology lacks effective overload protection mechanism, which cannot timely cut off the output of energy storage devices and start the cooling system when the short-circuit capacity exceeds the set threshold, posing a safety hazard.

[0009] Therefore, there is an urgent need for a low-voltage network-forming energy storage device overload capacity optimization method that can dynamically adjust the output power of energy storage devices according to the real-time changes of power system short-circuit capacity, and automatically start the protection mechanism when the short-circuit capacity exceeds the threshold. SUMMARY

[0010] In order to solve the problem that the existing energy storage devices often do not fully consider the overload demand under different short-circuit capacities when designing, which leads to problems such as device overload damage or system stability decline in actual operation, the present application provides a low-voltage network-forming energy storage device overload capacity optimization method and related equipment.

[0011] The technical solution adopted by the present application to solve its technical problems is: In a first aspect, the application provides a method for optimizing overload capacity of a low-voltage networked energy storage device, comprising: real-time collection of short-circuit current and voltage data of the power system, calculation of current short-circuit capacity from the short-circuit current and voltage data; automatic adjustment of output power of the energy storage device according to changes in the short-circuit capacity; when the short-circuit capacity exceeds a set threshold, cutting off the output of the energy storage device and starting the cooling system.

[0012] As a further improvement of the application, the real-time collection of short-circuit current and voltage data of the power system is achieved by sensors and monitoring devices installed in the power system.

[0013] As a further improvement of the application, the calculation of current short-circuit capacity from the short-circuit current and voltage data comprises: real-time collection of short-circuit current Isc and voltage Vsc of the power grid by sensors, and a short-circuit capacity calculation method is:

[0014] wherein S SC is the short-circuit capacity.

[0015] As a further improvement of the application, the automatic adjustment of output power of the energy storage device according to changes in the short-circuit capacity comprises: when the output power corresponding to the short-circuit capacity is greater than the maximum allowed output power, reducing the output power of the energy storage device; when the output power corresponding to the short-circuit capacity is less than the maximum allowed output power, increasing the output power of the energy storage device.

[0016] As a further improvement of the application, the calculation method of the maximum allowed output power is:

[0017] wherein P max is the maximum allowed output power of the energy storage device under the current short-circuit capacity; S sc is the current short-circuit capacity; and K is a safety factor.

[0018] As a further improvement of the application, the safety factor is dynamically adjusted according to the device temperature T and the ambient humidity H, specifically:

[0019] wherein K0 is a basic safety factor and △K is an adjustment range.

[0020] As a further improvement of the application, the actual output power of the energy storage device is adjusted as:

[0021] In the formula, P out is the actual output power of the energy storage device, P max is the maximum allowable output power of the energy storage device under the current short-circuit capacity, P demand is the power currently demanded by the power system.

[0022] As a further improvement of the present application, the charging and discharging strategy of the energy storage device is dynamically adjusted to P charge / discharge = α * Pout In the formula, α is the charging and discharging coefficient, -1 ≤ α ≤ 1, determined by the system demand; α > 0: discharging mode; α < 0: charging mode.

[0023] In a second aspect, the present application is a low-voltage network-type energy storage device overload capacity optimization module, comprising: An acquisition unit for real-time acquisition of short-circuit current and voltage data of the power system, and calculation of the current short-circuit capacity from the short-circuit current and voltage data; An adjustment unit for automatically adjusting the output power of the energy storage device according to the change in short-circuit capacity; A cutoff unit for cutting off the output of the energy storage device and starting the cooling system when the short-circuit capacity exceeds a set threshold.

[0024] As a further improvement of the present application, in the acquisition unit, the real-time acquisition of short-circuit current and voltage data of the power system is performed by sensors and monitoring devices installed in the power system.

[0025] As a further improvement of the present application, the calculation of the current short-circuit capacity from the short-circuit current and voltage data includes: Real-time acquisition of the short-circuit current I sc and voltage V sc of the power grid by sensors, and a short-circuit capacity calculation method:

[0026] In the formula, SSC is the short-circuit capacity.

[0027] As a further improvement of the present application, in the adjustment unit, the automatic adjustment of the output power of the energy storage device according to the change in short-circuit capacity includes: When the output power corresponding to the short-circuit capacity is greater than the maximum allowable output power, reducing the output power of the energy storage device; When the output power corresponding to the short-circuit capacity is less than the maximum allowable output power, increasing the output power of the energy storage device.

[0028] As a further improvement of the present application, the calculation method of the maximum allowable output power is:

[0029] In the formula, Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity; Ssc is the current short-circuit capacity; K is the safety factor.

[0030] As a further improvement of the present application, the safety factor is dynamically adjusted according to the device temperature T and the ambient humidity H, specifically:

[0031] In the formula, K0 is the basic safety factor, and △K is the adjustment range.

[0032] As a further improvement of the present application, the actual output power of the energy storage device is adjusted as:

[0033] In the formula, Pout is the actual output power of the energy storage device, Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, and Pdemand is the current demand of the power system.

[0034] As a further improvement of the present application, the charge-discharge strategy of the energy storage device is dynamically adjusted as P charge / discharge =α*Pout In the formula, α is the charge-discharge coefficient, -1 ≤ α ≤ 1, which is determined by the system demand; α>0: discharge mode; α<0: charge mode.

[0035] In a third aspect, the present application is a low-voltage network-type energy storage device overload capacity optimization system, comprising: A short-circuit capacity detection module for real-time detection of the short-circuit capacity in the power system; A control module for dynamically adjusting the overload capacity of the energy storage device according to the data provided by the short-circuit capacity detection module; it includes the low-voltage network-type energy storage device overload capacity optimization module; An overload protection module for automatically starting the overload protection mechanism; A data storage and analysis module for recording the operating data of the energy storage device under different short-circuit capacities.

[0036] In a fourth aspect, the present application is an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the low-voltage network-type energy storage device overload capacity optimization method.

[0037] In a fifth aspect, the present application provides a computer readable storage medium storing a computer program, which, when executed by a processor, implements the overload capacity optimization method for low-voltage grid-forming energy storage devices.

[0038] In a sixth aspect, the present application provides a computer program product comprising computer instructions for instructing a computer to execute the overload capacity optimization method for low-voltage grid-forming energy storage devices.

[0039] The present application has the following advantages: by real-time detection of short-circuit capacity and dynamic adjustment of the overload capacity of the energy storage device, the operation stability, reliability and efficiency of the device are significantly improved. Compared with the prior art, the present application can automatically adjust the output power of the energy storage device according to the actual situation under different short-circuit capacities, avoiding the risk of device overload damage, while ensuring the stable operation of the system. In addition, the present application also considers the influence of factors such as device temperature and environmental humidity on the overload capacity through a dynamic safety factor adjustment mechanism, further improving the adaptability and reliability of the system. Through data storage and analysis functions, the present application can also provide data support for long-term optimization of the device, prolonging the service life of the device, reducing energy consumption and operating costs, simplifying the operation and control process, and having significant environmental and energy-saving benefits.

[0040] Further, through real-time detection of short-circuit capacity and dynamic adjustment algorithm, precise control of the output power of the energy storage device is achieved.

[0041] Further, adaptive adjustment of the safety factor improves the adaptability and safety of the device in different environments. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present application or the prior art. It should be understood that the drawings in the following introduction are only for the convenience of clearly describing part of the embodiments of the technical solutions of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0043] Figure 1 A flow chart of the overload capacity optimization method for low-voltage grid-forming energy storage devices provided by the present application is provided. Figure 2 A low-voltage grid-forming energy storage device overload capacity optimization principle diagram provided by the embodiments of the present application is provided. Figure 3 A low-voltage grid-forming energy storage device overload capacity optimization system provided by the present application is provided. Figure 4 An electronic device schematic diagram provided by the present application is provided. DETAILED DESCRIPTION

[0044] The technical solutions of the present application will be described clearly and completely below by means of embodiments in conjunction with the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0045] Embodiment one As shown in Figure 1 and Figure 2 The first object of the present application is to provide a low-voltage network configuration type energy storage device overload capacity optimization method, comprising the following steps: Step one: real-time acquisition of short-circuit current and voltage data of the power system, and calculation of the current short-circuit capacity from the short-circuit current and voltage data; In this embodiment, the short-circuit current and voltage data of the power system are real-time acquired by sensors and monitoring devices installed in the power system. These sensors include current transformers and voltage transformers, which are installed at the input and output ends of the low-voltage network configuration type energy storage device respectively. The range of the current transformer is 0-1000A, and the accuracy is 0.5 level; the range of the voltage transformer is 0-1000V, and the accuracy is 0.5 level. These sensors are connected to the central control system through the data acquisition unit, and the sampling frequency is set to 100Hz to ensure that transient changes in the power grid can be captured.

[0046] The short-circuit current Isc and voltage Vsc of the power grid are real-time acquired by the sensors, and the short-circuit capacity calculation method is:

[0047] In the formula, Ssc is the short-circuit capacity, with the unit of kVA; Vsc is the short-circuit voltage, with the unit of kV; and Isc is the short-circuit current, with the unit of kA.

[0048] For example, when the measured short-circuit voltage Vsc is 0.38kV and the short-circuit current Isc is 5kA, the calculated short-circuit capacity Ssc is: Ssc = √3× 0.38 × 5 = 3.29kVA Step two: automatically adjusting the output power of the energy storage device according to the change of the short-circuit capacity; In this embodiment, the system automatically adjusts the output power of the energy storage device according to the change of the short-circuit capacity. The specific adjustment strategy is as follows: When the output power corresponding to the short-circuit capacity is greater than the maximum allowed output power, the output power of the energy storage device is reduced; When the output power corresponding to the short-circuit capacity is less than the maximum allowable output power, the output power of the energy storage device is increased.

[0049] The maximum allowable output power calculation method is:

[0050] Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, with the unit of kW; Ssc is the current short-circuit capacity, with the unit of kVA; K is the safety factor, dimensionless.

[0051] The safety factor is dynamically adjusted according to the device temperature T and the environmental humidity H, and is specifically:

[0052] In the formula, K0 is the basic safety factor, with the value of 0.8; ΔK is the adjustment amplitude, with the value of 0.3; T is the current device temperature, with the unit of ℃; Tmax is the maximum allowable temperature of the device, with the value of 85 ℃; H is the current environmental humidity, with the unit of %RH; Hmax is the maximum environmental humidity, with the value of 95 %RH.

[0053] When the short-circuit capacity Ssc is 3.29 kVA, the maximum allowable output power Pmax is: Pmax = 0.689 × 3.29 = 2.27 kW The actual output power of the energy storage device is adjusted as: Pout = min(Pmax, Pdemand) In the formula, Pout is the actual output power of the energy storage device, with the unit of kW; Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, with the unit of kW; Pdemand is the current demand power of the power system, with the unit of kW.

[0054] For example, when the current demand power Pdemand of the power system is 2.5 kW, and the maximum allowable output power Pmax is 2.27 kW, the actual output power Pout of the energy storage device should be adjusted as: Pout = min(2.27, 2.5) = 2.27 kW The charge-discharge strategy of the energy storage device is dynamically adjusted as: Pcharge / discharge = α × Pout α is the charge-discharge coefficient, -1 ≤ α ≤ 1, which is determined by the system demand; α > 0 represents the discharging mode; α < 0 represents the charging mode; α = 0 represents the standby mode.

[0055] For example, when the system needs to discharge the energy storage device, set α = 0.8, then: Pcharge / discharge = 0.8 × 2.27 = 1.816kW When the system needs to charge the energy storage device, set α = -0.6, then: Pcharge / discharge = -0.6 × 2.27 = -1.362kW Step three: when the short-circuit capacity exceeds the set threshold, cut off the output of the energy storage device and start the cooling system.

[0056] In this embodiment, the short-circuit capacity threshold Ssc_threshold is set to 10kVA. When the detected short-circuit capacity Ssc exceeds 10kVA, the system immediately performs the following operations: First, control the relay to cut off the output circuit of the energy storage device to ensure that the device is isolated from the grid; Second, start the cooling system, including the fan and the liquid cooling system. The fan speed is set to 3000rpm and the liquid cooling system flow rate is set to 10L / min; Finally, the system enters a protection state and sends an alarm message to the administrator.

[0057] After the cooling system is started, the device temperature is continuously monitored. When the device temperature drops to a safe temperature (below 60℃) and the short-circuit capacity returns to normal (below 90% of the threshold, i.e. 9kVA), the system can be restarted and normal operation can be resumed.

[0058] In practical applications, this method monitors the short-circuit capacity in real time and dynamically adjusts the output power of the energy storage device, effectively preventing the risk of damage to the energy storage device due to overload in a low-voltage grid. At the same time, by introducing a safety factor dynamic adjustment mechanism based on temperature and humidity, the adaptability and safety of the system are further improved.

[0059] The above scheme of the present application can enhance the stability of the power grid, prevent voltage collapse, actively reduce the load when the short-circuit capacity is insufficient, and avoid local voltage drop caused by energy storage overload. By limiting the frequency deviation to within ±0.2Hz through inertia adjustment, the requirements of GB / T 31464-2015 "Power Grid Operation Guidelines" are met. Compared with fixed power operation, adaptive adjustment can increase the annual equivalent utilization hours of energy storage by 20% to 35%. Avoiding frequent deep charging and discharging, the battery cycle number is increased by more than 30%. The new energy consumption capacity is improved, and as the main power source, the voltage reference is constructed to shorten the system recovery time.

[0060] Embodiment two A low-voltage network type energy storage device overload capacity optimization module, comprising: An acquisition unit is configured to collect short-circuit current and voltage data of the power system in real time, and calculate a current short-circuit capacity based on the short-circuit current and voltage data; An adjustment unit is configured to automatically adjust an output power of the energy storage device according to a change in the short-circuit capacity. A cut-off unit is configured to cut off the output of the energy storage device and start a cooling system when the short-circuit capacity exceeds a set threshold.

[0061] In this embodiment, the acquisition unit includes a plurality of sensor interfaces and a data processing circuit. The sensor interfaces are configured to connect current transformers and voltage transformers installed in the power system to collect short-circuit current and voltage data of the power system. The data processing circuit uses a 32-bit ARM Cortex-M4 processor with a main frequency of 168 MHz and a built-in 12-bit ADC converter with a sampling rate of 1 MSPS to convert the collected analog signals into digital signals and calculate the current short-circuit capacity based on the short-circuit current and voltage data.

[0062] The acquisition unit collects the short-circuit current Isc and voltage Vsc of the power grid in real time through sensors, and the short-circuit capacity calculation method is as follows: (unit: kVA) In the formula, Ssc is the short-circuit capacity with a unit of kVA, Vsc is the short-circuit voltage with a unit of kV, and Isc is the short-circuit current with a unit of kA.

[0063] The adjustment unit includes a power calculation module and a power control module 200. The power calculation module calculates the maximum allowable output power of the energy storage device based on the short-circuit capacity data provided by the acquisition unit. The power control module 200 adjusts the actual output power of the energy storage device based on the maximum allowable output power and the current demand of the power system.

[0064] The power calculation module of the adjustment unit calculates the maximum allowable output power using the following method:

[0065] Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, with a unit of kW; Ssc is the current short-circuit capacity, with a unit of kVA; and K is a safety factor, dimensionless.

[0066] The safety factor is dynamically adjusted based on the device temperature T and the environmental humidity H, and is specifically as follows:

[0067] In the formula, K0 is the basic safety factor, the value is 0.8; ΔK is the adjustment range, the value is 0.3; T is the current device temperature, the unit is ℃; Tmax is the maximum allowable temperature of the device, the value is 85℃; H is the current environmental humidity, the unit is %RH; Hmax is the maximum environmental humidity, the value is 95%RH.

[0068] The power control module 200 of the adjustment unit adjusts the actual output power of the energy storage device according to the following formula: Pout = min(Pmax, Pdemand) In the formula, Pout is the actual output power of the energy storage device, the unit is kW; Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, the unit is kW; Pdemand is the current demand of the power system, the unit is kW.

[0069] The cut-off unit includes a relay control circuit and a cooling system control circuit. The relay control circuit is used to cut off the output circuit of the energy storage device when the short-circuit capacity exceeds the set threshold. The cooling system control circuit is used to start the cooling system, including the fan and the liquid cooling system.

[0070] The cut-off unit sets the short-circuit capacity threshold Ssc_threshold to 10kVA. When the detected short-circuit capacity Ssc exceeds 10kVA, the cut-off unit performs the following operations: Cut off the output circuit of the energy storage device through the relay control circuit to ensure that the device is isolated from the grid; Start the cooling system, including the fan and the liquid cooling system, through the cooling system control circuit; Send alarm information to the management system.

[0071] After the cooling system is started, the cut-off unit continuously monitors the device temperature. When the device temperature drops to the safe temperature (below 60℃) and the short-circuit capacity returns to normal (below 90% of the threshold, i.e. 9kVA), the cut-off unit can restart the energy storage device to resume normal operation.

[0072] Embodiment three As Figure 3 shown, the third object of the application is to provide a low-voltage network type energy storage device overload capacity optimization system, comprising: A short-circuit capacity detection module 100 for real-time detection of short-circuit capacity in the power system; A control module 200 for dynamically adjusting the overload capacity of the energy storage device according to the data provided by the short-circuit capacity detection module 100; it includes the low-voltage network type energy storage device overload capacity optimization module described in embodiment two; An overload protection module 300 for automatically starting the overload protection mechanism; The data storage and analysis module 400 is used for recording the operation data of the energy storage device under different short-circuit capacities.

[0073] In the embodiment, the short-circuit capacity detection module 100 includes a sensor network distributed at key nodes of the power system and a central data acquisition system. The sensor network is composed of a plurality of current transformers and voltage transformers for collecting current and voltage data of each node of the power system. The central data acquisition system adopts an industrial computer equipped with an Intel Core i5 processor, 8 GB RAM, 256 GB SSD storage, and running a real-time operating system, for receiving data transmitted by the sensor network and calculating the short-circuit capacity of the power system.

[0074] The short-circuit capacity detection module 100 calculates the short-circuit capacity by the following method: (unit: kVA); In the formula, Ssc is the short-circuit capacity, with a unit of kVA; Vsc is the short-circuit voltage, with a unit of kV; and Isc is the short-circuit current, with a unit of kA.

[0075] The control module 200 adopts an embedded system based on ARM Cortex-A53, with a main frequency of 1.2 GHz, equipped with 2 GB RAM and 32 GB eMMC storage, and running a customized Linux operating system. The control module 200 receives data provided by the short-circuit capacity detection module 100 and dynamically adjusts the overload capacity of the energy storage device according to the data.

[0076] The control module 200 includes the overload capacity optimization module of the low-voltage networked energy storage device described in Embodiment Two, specifically including: An acquisition unit for collecting short-circuit current and voltage data of the power system in real time, and calculating the current short-circuit capacity from the short-circuit current and voltage data; An adjustment unit for automatically adjusting the output power of the energy storage device according to the change of the short-circuit capacity; A cut-off unit for cutting off the output of the energy storage device and starting the cooling system when the short-circuit capacity exceeds a set threshold.

[0077] The control module 200 calculates the maximum allowable output power of the energy storage device according to the data provided by the short-circuit capacity detection module 100 by the following method: ; Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, with a unit of kW; Ssc is the current short-circuit capacity, with a unit of kVA; and K is a safety factor, dimensionless.

[0078] The safety factor is dynamically adjusted according to the device temperature T and the environmental humidity H, specifically: ; In the formula, K0 is the basic safety factor, and the value is 0.8; ΔK is the adjustment amplitude, and the value is 0.3; T is the current device temperature, the unit is ℃; Tmax is the maximum allowable temperature of the device, and the value is 85℃; H is the current environmental humidity, the unit is %RH; Hmax is the maximum environmental humidity, and the value is 95%RH.

[0079] The control module 200 adjusts the actual output power of the energy storage device according to the following formula: Pout = min(Pmax, Pdemand); In the formula, Pout is the actual output power of the energy storage device, the unit is kW; Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, the unit is kW; Pdemand is the current demand of the power system, the unit is kW.

[0080] The overload protection module 300 includes a hardware protection circuit and a software protection algorithm. The hardware protection circuit includes an overcurrent protector, an overvoltage protector and a temperature protector, which are used to quickly cut off the output of the energy storage device in an emergency. The software protection algorithm runs on the control module 200, which is used to cut off the output of the energy storage device by controlling the relay and start the cooling system when an abnormal situation is detected.

[0081] More specifically, the overload protection module 300 300: this module is used to automatically start the overload protection mechanism when the short-circuit capacity is too large, to prevent the energy storage device from being damaged due to overload. The overload protection module 300 is linked with the control module 200, and when it is detected that the short-circuit capacity exceeds the set threshold, the overload protection module 300 will automatically cut off the output of the energy storage device and start the cooling system to prevent the device from overheating. The short-circuit capacity threshold S th Dynamic adjustment based on device rated parameters: S th =K safe × S rated S rated : rated short-circuit capacity of the energy storage device (unit: MVA); K sare : safety factor, the value range is 1.2~1.5; The current short-circuit capacity S SC is calculated in real time by the short-circuit capacity detection module 100, and compared with S th : If S sc >S th , trigger overload protection If S sc <S th , keep normal operation.

[0082] As an example, the automatic start of the overload protection mechanism can include: Power cut-off: using a hybrid DC circuit breaker (HB-DCCB) combining mechanical switches and power electronic devices to achieve current interruption within 5ms.

[0083] The liquid cooling system flow rate Q is dynamically adjusted according to the temperature T:

[0084] The base flow rate (Qbase); represents the cooling demand of the device at normal operating temperature (T = Tref).

[0085] The flow rate adjustment increment (△Q): When the temperature rises, the cooling system needs to increase the upper limit of the flow rate. For example, when the temperature reaches Tmax, the flow rate increases to Qbase +△Q The reference temperature (Tref) represents the normal operating temperature of the device; The temperature difference (T-Tref) reflects the degree of deviation of the current temperature from the reference value.

[0086] The upper limit of the temperature (Tmax): When T < Tref, the flow rate remains at the base value Qbase; when T > Tref, the flow rate increases proportionally and linearly until T = Tmax, reaching the maximum flow rate.

[0087] Thermal management: start the variable frequency control of the liquid cooling system to increase the flow rate of the cooling liquid from 5L / min to 20L / min, and cooperate with the phase change material (PCM) to absorb transient heat.

[0088] State feedback: upload the protection log to the EMS through the CAN bus, including the trigger time, action sequence, and device state snapshot.

[0089] The overload protection module 300 sets multiple protection thresholds: Primary protection: when the short-circuit capacity exceeds 8kVA but is less than 10kVA, reduce the output power of the energy storage device to 80% of the maximum allowed output power; Secondary protection: when the short-circuit capacity exceeds 10kVA, cut off the output of the energy storage device and start the cooling system; Tertiary protection: when the device temperature exceeds 75℃, regardless of the short-circuit capacity, cut off the output of the energy storage device and start the cooling system.

[0090] The data storage and analysis module 400 adopts a distributed database system, including a real-time database and a historical database. The real-time database is used to store the running data of the last 24 hours, with a sampling interval of 1 second. The historical database is used to store the long-term running data, with a sampling interval of 1 minute.

[0091] The data storage and analysis module 400 records the following data: Short-circuit capacity data: including short-circuit current, short-circuit voltage and calculated short-circuit capacity; Energy storage device running data: including output power, charge and discharge state, device temperature, etc. Environmental data: including environmental temperature, humidity, etc. System events: including protection actions, alarm information, etc.

[0092] The data storage and analysis module 400 also provides data analysis functions, including trend analysis, correlation analysis and anomaly detection. These analysis results can help the operation and maintenance personnel to optimize system parameters, improve the reliability and efficiency of the system.

[0093] Embodiment four As shown in Figure 4 The fourth object of the embodiment of the application is to provide an electronic device, including a memory 701, a processor 702, and a computer program stored in the memory 701 and executable on the processor, wherein the processor executes the computer program to realize the low-voltage networked energy storage device overload capacity optimization method described above. It also includes a communication interface 703 and a bus 704.

[0094] The computer program stored in the memory includes the following modules: Data acquisition module: responsible for reading current, voltage, temperature, humidity and other data from sensors; Data processing module: responsible for processing the collected data, calculating short-circuit capacity and maximum allowable output power; Control decision module: responsible for determining the output power and charge and discharge strategy of the energy storage device according to the processing result; Execution control module 200: responsible for converting control decisions into specific control signals to control the operation of the energy storage device; Data storage module: responsible for storing running data into the database; Communication module: responsible for data exchange with other systems; User interface module: provides a Web interface for operation and maintenance personnel to monitor and manage the system.

[0095] When the processor executes the computer program, it realizes the low-voltage networked energy storage device overload capacity optimization method described in embodiment one, specifically including: Step one: Real-time acquisition of short-circuit current and voltage data of the power system, and calculation of the current short-circuit capacity from the short-circuit current and voltage data; The data acquisition module reads sensor data through a data acquisition card, with a sampling frequency of 100 Hz. The data processing module receives these data and calculates the short-circuit capacity through the following formula: (unit: kVA) In the formula, Ssc is the short-circuit capacity, with a unit of kVA; Vsc is the short-circuit voltage, with a unit of kV; and Isc is the short-circuit current, with a unit of kA.

[0096] Step two: Automatic adjustment of the output power of the energy storage device according to the change in the short-circuit capacity; The control decision module calculates the maximum allowable output power through the following formula according to the short-circuit capacity calculated by the data processing module:

[0097] Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, with a unit of kW; Ssc is the current short-circuit capacity, with a unit of kVA; and K is the safety factor, dimensionless.

[0098] The safety factor is dynamically adjusted according to the device temperature T and the environmental humidity H, and is specifically:

[0099] In the formula, K0 is the basic safety factor, with a value of 0.8; ΔK is the adjustment amplitude, with a value of 0.3; T is the current device temperature, with a unit of ℃; Tmax is the maximum allowable temperature of the device, with a value of 85 ℃; H is the current environmental humidity, with a unit of %RH; and Hmax is the maximum environmental humidity, with a value of 95%RH.

[0100] The control decision module determines the actual output power of the energy storage device according to the following formula:

[0101] In the formula, Pout is the actual output power of the energy storage device, with a unit of kW; Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, with a unit of kW; and Pdemand is the current demand of the power system, with a unit of kW.

[0102] The execution control module 200 converts the control decision into a specific control signal, controls the power converter of the energy storage device through a digital output interface, and adjusts the output power of the energy storage device.

[0103] Step three: When the short-circuit capacity exceeds the set threshold, the output of the energy storage device is cut off, and the cooling system is started.

[0104] The control decision module continuously monitors the short-circuit capacity. When the detected short-circuit capacity Ssc exceeds the set threshold 10 kVA, the control decision module immediately generates a shutdown instruction. After the execution control module 200 receives the shutdown instruction, it controls the relay through the digital output interface, shuts down the output circuit of the energy storage device, and starts the cooling system.

[0105] The data storage module stores the data of the entire process into the database, including the short-circuit capacity, the maximum allowed output power, the actual output power, the device temperature, the environmental humidity, etc. These data can be viewed and analyzed through the Web interface provided by the user interface module.

[0106] Example Five A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the low-voltage networked energy storage device overload capacity optimization method of example one.

[0107] In this embodiment, the computer-readable storage medium is a non-volatile storage medium, which can be a ROM, a RAM, a magnetic disk, or an optical disk, etc. In this embodiment, the computer-readable storage medium is a 512 GB NVMe SSD, which adopts 3D NAND flash technology and has high-speed read-write performance and long-life characteristics.

[0108] The computer program stored in the computer-readable storage medium is written in C++ language and generates an executable file after compilation. The program structure includes the following main parts: Header file part: contains the standard library and custom header files required by the program; Constant definition part: defines the constants used in the program, such as the safety factor K0, the adjustment amplitude ΔK, the maximum allowed temperature Tmax, the maximum environmental humidity Hmax, etc. Class definition part: defines the classes used in the program, such as the data acquisition class, the data processing class, the control decision class, the execution control class, etc. Main function part: the entry point of the program, responsible for initializing the system, creating instances of each class, and starting the main loop.

[0109] When the computer program is executed by the processor, the low-voltage networked energy storage device overload capacity optimization method of example one is implemented, which specifically includes: Step one: real-time acquisition of short-circuit current and voltage data of the power system, and calculation of the current short-circuit capacity from the short-circuit current and voltage data; The computer program calls the hardware driver interface through an instance of the data acquisition class to read sensor data. The data acquisition class collects data every 10 milliseconds and passes the data to the data processing class. The data processing class receives the data and calculates the short circuit capacity using the following formula: (unit: kVA) In the formula, Ssc is the short circuit capacity, with a unit of kVA; Vsc is the short circuit voltage, with a unit of kV; and Isc is the short circuit current, with a unit of kA.

[0110] Step two: automatically adjust the output power of the energy storage device according to the change in short circuit capacity; The computer program calculates the maximum allowable output power through an instance of the control decision class according to the short circuit capacity calculated by the data processing class using the following formula:

[0111] Pmax is the maximum allowable output power of the energy storage device under the current short circuit capacity, with a unit of kW; Ssc is the current short circuit capacity, with a unit of kVA; and K is the safety factor, which is dimensionless.

[0112] The safety factor is dynamically adjusted according to the device temperature T and the environmental humidity H, and is specifically:

[0113] In the formula, K0 is the basic safety factor, with a value of 0.8; ΔK is the adjustment amplitude, with a value of 0.3; T is the current device temperature, with a unit of ℃; Tmax is the maximum allowable temperature of the device, with a value of 85 ℃; H is the current environmental humidity, with a unit of %RH; and Hmax is the maximum environmental humidity, with a value of 95 %RH.

[0114] The control decision class determines the actual output power of the energy storage device according to the following formula: Pout = min(Pmax, Pdemand) In the formula, Pout is the actual output power of the energy storage device, with a unit of kW; Pmax is the maximum allowable output power of the energy storage device under the current short circuit capacity, with a unit of kW; and Pdemand is the current demand of the power system, with a unit of kW.

[0115] The computer program converts the control decision into a specific control signal through an instance of the execution control class, controls the power converter of the energy storage device through the hardware interface, and adjusts the output power of the energy storage device.

[0116] Step three: when the short circuit capacity exceeds a set threshold, the output of the energy storage device is cut off, and the cooling system is started.

[0117] The computer program continuously monitors the short-circuit capacity by controlling instances of the decision-making class. When the detected short-circuit capacity Ssc exceeds the set threshold of 10 kVA, the control decision class immediately generates a shutdown instruction. After receiving the shutdown instruction, the execution control class controls the relay through the hardware interface, shuts down the output circuit of the energy storage device, and starts the cooling system.

[0118] The computer program also includes a data recording function, which records key data during operation into log files, including timestamps, short-circuit capacity, maximum allowed output power, actual output power, device temperature, and environmental humidity. These log files are stored in computer-readable storage media and can be used for subsequent analysis and troubleshooting.

[0119] Embodiment Six A computer program product, comprising computer instructions instructing a computer to execute the low-voltage grid-forming energy storage device overload capacity optimization method described in Embodiment One.

[0120] In this embodiment, the computer program product is a software package, including an installer, a main program, configuration files, help documents, and sample data. The software package is released in the form of an ISO image file and can be installed on the target computer through a CD or USB flash drive.

[0121] The computer instructions in the computer program product are written in multiple programming languages, including C++ (core algorithms and hardware interfaces), Python (data analysis and visualization), and JavaScript (Web interface). The program architecture adopts modular design, including the following main modules: Core module: responsible for implementing the core algorithm of the low-voltage grid-forming energy storage device overload capacity optimization method; Hardware interface module: responsible for communication with sensors and control devices; Data storage module: responsible for storing running data into a database; Data analysis module: responsible for analyzing historical data and extracting useful information; Visualization module: responsible for displaying data in chart form; Web service module: provides a Web interface for users to remotely access and control the system.

[0122] The computer instructions instruct the computer to execute the low-voltage grid-forming energy storage device overload capacity optimization method described in Embodiment One, specifically including: Step One: Real-time acquisition of short-circuit current and voltage data of the power system, and calculation of the current short-circuit capacity from the short-circuit current and voltage data; The computer instructions call the sensor driver program through the hardware interface module to read the data of the current transformer and the voltage transformer. The hardware interface module collects data every 10 milliseconds and transmits the data to the core module. The core module receives the data and calculates the short-circuit capacity by the following formula: Ssc = √3 × Vsc × Isc In the formula, Ssc is the short-circuit capacity, with the unit of kVA; Vsc is the short-circuit voltage, with the unit of kV; and Isc is the short-circuit current, with the unit of kA.

[0123] Step two: automatically adjust the output power of the energy storage device according to the change of the short-circuit capacity; The computer instructions calculate the maximum allowable output power of the energy storage device according to the calculated short-circuit capacity by the following formula through the core module:

[0124] Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, with the unit of kW; Ssc is the current short-circuit capacity, with the unit of kVA; and K is the safety factor, dimensionless.

[0125] The safety factor is dynamically adjusted according to the device temperature T and the environmental humidity H, and is specifically:

[0126] In the formula, K0 is the basic safety factor, with the value of 0.8; ΔK is the adjustment amplitude, with the value of 0.3; T is the current device temperature, with the unit of ℃; Tmax is the maximum allowable temperature of the device, with the value of 85 ℃; H is the current environmental humidity, with the unit of %RH; and Hmax is the maximum environmental humidity, with the value of 95 %RH.

[0127] The core module determines the actual output power of the energy storage device according to the following formula: Pout = min(Pmax, Pdemand) In the formula, Pout is the actual output power of the energy storage device, with the unit of kW; Pmax is the maximum allowable output power of the energy storage device under the current short-circuit capacity, with the unit of kW; and Pdemand is the current demand of the power system, with the unit of kW.

[0128] The computer instructions convert the control decision into specific control signals through the hardware interface module, and control the power converter of the energy storage device through the hardware interface to adjust the output power of the energy storage device.

[0129] Step three: when the short-circuit capacity exceeds the set threshold, the output of the energy storage device is cut off, and the cooling system is started.

[0130] The computer instructions continuously monitor the short-circuit capacity through the core module. When the detected short-circuit capacity Ssc exceeds the set threshold of 10 kVA, the core module immediately generates a shutdown instruction. After receiving the shutdown instruction, the hardware interface module controls the relay through the hardware interface to shut down the output circuit of the energy storage device and start the cooling system.

[0131] The computer instructions store key data during operation into the database through the data storage module, including time stamp, short-circuit capacity, maximum allowed output power, actual output power, device temperature, and environmental humidity. The data analysis module regularly analyzes these data to generate statistical reports and trend charts. The visualization module displays these reports and charts on the Web interface for users to view and analyze.

[0132] It should be noted that Embodiment One, Embodiment Two, Embodiment Three, Embodiment Four, Embodiment Five, and Embodiment Six are all low-voltage networked energy storage device overload capacity optimization methods.

[0133] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices, which realize the functions specified in the flow Figure 1 one or more flows and / or blocks in the flow Figure 1 one or more blocks or multiple blocks.

[0134] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in the flow Figure 1 one or more flows and / or blocks in the flow Figure 1 one or more blocks or multiple blocks.

[0135] The present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, readable storage media, optical storage, etc.) containing computer usable program code.

[0136] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks or in conjunction with the flowcharts. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks.

[0137] Obviously, the embodiments described above are only part but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work should belong to the protection scope of the present application.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing overload capability of a low-voltage grid-forming energy storage device, characterized in that, The application relates to a low-voltage network type energy storage device overload capacity optimization method. Real-time acquisition of short-circuit current and voltage data of a power system, calculation of current short-circuit capacity from the short-circuit current and voltage data; Automatic adjustment of the output power of the energy storage device according to the change of the short-circuit capacity; When the short-circuit capacity exceeds a set threshold, the output of the energy storage device is cut off, and a cooling system is started.

2. The method for optimizing overload capability of low-voltage meshed energy storage device according to claim 1, characterized in that, The real-time acquisition of short-circuit current and voltage data of the power system is realized by sensors and monitoring devices installed in the power system.

3. The method for optimizing overload capability of low-voltage meshed energy storage device according to claim 1, characterized in that, The calculation of the current short-circuit capacity from the short-circuit current and voltage data comprises: The short-circuit current I of the power grid is collected in real time by a sensor sc and the voltage V sc The short-circuit capacity calculation method is: In the formula, S SC is the short circuit capacity.

4. The method for optimizing overload capability of low-voltage meshed energy storage device according to claim 1, characterized in that, The automatic adjustment of the output power of the energy storage device according to the change of the short-circuit capacity comprises: When the output power corresponding to the short-circuit capacity is greater than the maximum allowable output power, the output power of the energy storage device is reduced; When the output power corresponding to the short-circuit capacity is less than the maximum allowable output power, the output power of the energy storage device is increased.

5. The method for optimizing overload capability of low-voltage meshed energy storage device according to claim 1, characterized in that, The calculation method of the maximum allowable output power is: In the formula, P max is the maximum allowable output power of the energy storage device under the current short-circuit capacity; S sc is the current short-circuit capacity; K is a safety factor.

6. The method for optimizing overload capability of low-voltage meshed energy storage device according to claim 5, characterized in that, The safety factor is dynamically adjusted according to the device temperature T and the environmental humidity H, and the safety factor is specifically: In the formula, K0 is a basic safety factor, and △K is an adjustment range.

7. The method for optimizing overload capability of low-voltage meshed energy storage device according to claim 1, characterized in that, The actual output power adjustment of the energy storage device is: In the formula, P out is the actual output power of the energy storage device, P max is the maximum allowable output power of the energy storage device at the current short-circuit capacity, P demand is the power currently demanded by the power system.

8. The method for optimizing overload capability of low-voltage meshed energy storage device according to claim 1, characterized in that, The charge-discharge strategy dynamic adjustment of the energy storage device is: P charge / discharge =α*Pout In the formula, alpha is a charge-discharge coefficient, -1 <= alpha <= 1, and the value of alpha is determined by system demand; alpha > 0: discharging mode; alpha < 0: charging mode.

9. A low voltage grid forming energy storage device overload capability optimization module based on the low voltage grid forming energy storage device overload capability optimization method of any of claims 1-8, characterized in that, The application relates to a low-voltage network type energy storage device overload capacity optimization method. An acquisition unit is used for real-time acquisition of short-circuit current and voltage data of a power system, and calculation of current short-circuit capacity from the short-circuit current and voltage data; An adjustment unit is used for automatic adjustment of the output power of the energy storage device according to the change of the short-circuit capacity; A cutting unit is used for cutting off the output of the energy storage device when the short-circuit capacity exceeds a set threshold, and starting a cooling system.

10. A system for optimizing overload capability of a low-voltage grid-forming energy storage device, comprising: The application relates to a low-voltage network type energy storage device overload capacity optimization method. The application relates to a low-voltage network type energy storage device overload capacity optimization method. The application relates to a low-voltage network type energy storage device overload capacity optimization method. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the low-voltage network type energy storage device overload capacity optimization method. The computer instruction instructs the computer to execute the low-voltage network type energy storage device overload capacity optimization method.

11. An electronic device, comprising: ​ 12. A computer-readable storage medium, characterized in that, ​ 13. A computer program product comprising computer instructions, characterized in that, ​

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