Base station energy storage equipment management method and system capable of self-adapting to output voltage

Abstract

The invention relates to the technical field of equipment management, in particular to a base station energy storage equipment management method and system capable of self-adapting to output voltage, and the method comprises the steps: obtaining the criticality static weight of electric equipment of a base station; obtaining a dynamic voltage demand function curve of base station electric equipment, an energy consumption function curve of a base station power supply system and an independent conversion loss function curve of a power supply; constructing a voltage distribution balance degree model of the energy storage equipment, and optimizing the voltage distribution balance degree model through a multi-objective optimization function to obtain a balanced distribution scheme; and inputting the balanced distribution scheme into the energy storage equipment management model, generating a voltage regulation and control instruction set, and completing time-sharing voltage regulation and control management of the energy storage equipment according to the voltage regulation and control instruction set. The problem that the output voltage of the energy storage equipment of the base station cannot be adaptively regulated and controlled according to the working condition of the electric equipment in the base station in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and is a method and system for managing a base station energy storage device with an adaptive output voltage. Background Art

[0002] With the rapid development of communication technology, the number of base stations is increasing continuously, posing higher requirements for the stability and reliability of base station power supply. In the base station power supply system, energy storage devices play a crucial role. When the mains power is interrupted or the voltage is unstable due to weather reasons, the energy storage devices can provide continuous power support for the base stations. However, the output voltage of existing base station energy storage devices is often fixed and cannot be adaptively adjusted according to the actual power consumption requirements of the base stations and the voltage conditions of the mains power. In some cases, the fixed output voltage may not meet the voltage requirements of different devices in the base station, resulting in low working efficiency or even damage of the devices; when the mains voltage fluctuates greatly, the energy storage devices cannot adjust the output voltage in time, which may affect the normal operation of the base stations and increase the risk of communication interruption. In addition, the energy storage devices with fixed output voltage may have relatively large losses during the energy conversion process, reducing the energy utilization efficiency. Summary of the Invention

[0003] The technical problem to be solved by the present invention is that in the prior art, the present invention solves the problem that the output voltage of the base station energy storage device in the prior art cannot be adaptively regulated according to the working conditions of the electrical equipment in the base station, and provides a method and system for managing a base station energy storage device with an adaptive output voltage.

[0004] In order to achieve the above object, the technical solution of a method for managing a base station energy storage device with an adaptive output voltage according to the present invention includes the following steps: S1: Obtain the criticality static weight of the base station electrical equipment according to the equipment attributes and historical voltage demand data of the base station electrical equipment; S2: Obtain the dynamic voltage demand function curve of the base station electrical equipment, the energy consumption function curve of the base station power supply system, and the independent conversion loss function curve of the power supply source; S3: According to S2, construct a voltage distribution balance model of the energy storage device, and at the same time obtain the safe voltage range of the base station electrical equipment as a constraint condition of the voltage distribution balance model; S4: Optimize the voltage distribution balance model through a multi-objective optimization function to obtain an equilibrium distribution scheme; S5: Input the equilibrium distribution scheme into the energy storage device management model to generate a set of voltage regulation instructions, and complete the time-sharing voltage regulation management of the energy storage device according to the set of voltage regulation instructions.

[0005] Specifically, S1 includes: S11: Obtain the attribute list of the base station power-consuming equipment, and calculate the static weight of the criticality of the base station power-consuming equipment according to the historical voltage demand data under different working modes of the base station power-consuming equipment. Preferably, the calculation strategy of the static weight of the criticality is specifically: ; Among them, is the static weight of the criticality; is the adaptive coefficient; is the current harmonic distortion rate of the power-consuming equipment; is the heating rate inside the cabinet of the power-consuming equipment; is the service priority; are the real-time information throughput and the maximum information throughput of the power-consuming equipment respectively; S12: Based on the dynamic voltage demand curves corresponding to each power-consuming equipment in the static weight of the criticality of the power-consuming equipment, obtain the voltage sensitivity coefficient and the power consumption curve of the corresponding power-consuming equipment.

[0006] Preferably, the calculation strategy of the voltage sensitivity coefficient of the power-consuming equipment is: ; Among them, is the voltage sensitivity coefficient of the power-consuming equipment; It should be noted that The power consumption curve corresponding to the power-consuming equipment represents the power consumption of the power-consuming equipment under different working voltages.

[0007] Specifically, S2 includes: S21: Define the time window parameter of the target power supply of the energy storage equipment according to the communication guarantee level and the importance weight of the power-consuming equipment ; Among them, represents the minimum guarantee start time of the critical power-consuming equipment with the highest service priority, represents the longest switching delay of the energy storage equipment; S22: Obtain the reference value of the dynamic voltage regulation rate according to the stable time window of the target power supply and the safe voltage range of the power-consuming equipment; Preferably, the stable time window of the target power supply is specifically: ; Among them, is the stable time window of the target power supply; Preferably, the calculation strategy of the reference value of the dynamic voltage regulation rate is: ; Among them, is the reference value of the dynamic voltage regulation rate; is the safe voltage range for the electrical equipment; S23: Differentiate the dynamic voltage demand function of the base station electrical equipment with respect to time to obtain the instantaneous voltage demand gradient of each electrical equipment. Preset the instantaneous voltage demand fluctuation threshold, and screen the time points at which the instantaneous voltage demand gradient of each electrical equipment exceeds the instantaneous voltage demand fluctuation threshold as the voltage regulation trigger signal points.

[0008] Specifically, S2 further includes: S24: Classify the historical cooperative voltage regulation strategies, voltage regulation margins, and corresponding energy consumption characteristics of the mains power and energy storage devices through a clustering algorithm according to the historical cooperative voltage regulation strategies, voltage regulation margins, and corresponding energy consumption characteristics of the mains power and energy storage devices to obtain several historical operation scenarios; Assign initial voltage values to several historical operation scenarios and determine the initial power supply voltage value of the mains power supply; Preferably, ; is the initial power supply voltage value of the mains power supply; wherein, the power consumption of the mains power at voltage V. It should be noted that the higher the voltage, the greater the energy consumption; is the current voltage and the deviation from the standard voltage . It should be noted that the greater the deviation, the more unstable the power supply direction coefficient; if = 0, it means that the power supply scheme pursues power saving; if is very large, it means that the power supply scheme gives priority to ensuring power supply stability; S25: Continuously monitor the dynamic voltage demand function of the base station electrical equipment. When the voltage regulation trigger signal point is monitored. It should be noted that when the voltage regulation trigger signal point is monitored, calculate the voltage regulation urgency through the fuzzy decision layer , and determine the initial power supply scheme through the average value of the voltage regulation urgency of the base station electrical equipment; When < 0.3, only fine-tune the mains power through PID control and continue to execute step S26 When 0.3 ≤ < 0.7, enable the pre-synchronization of the energy storage device and execute step S28 at the same time; When ≥ 0.7, force the cooperative voltage regulation mechanism of the mains power and the energy storage device and execute step S28 at the same time; Preferably, the calculation strategy of the voltage regulation urgency is: ; wherein, is the voltage regulation urgency for the i-th electrical equipment; is the deviation value between the electricity demand and the actual power supply, is the baseline of the electricity demand deviation; Record the energy consumption evolution variable generated by the dynamic switching process and the independent conversion loss evolution variable of the power supply source, and update the energy consumption function curve of the base station power supply system and the independent conversion loss function curve of the power supply source.

[0009] Specifically, S2 further includes: S26: Calculate the mains energy voltage regulation margin based on the voltage demand change gradient and the dynamic voltage regulation rate reference value, and at the same time predict the change amount of the voltage demand of the base station electrical equipment; Select the depletion critical point according to the mains energy voltage regulation margin and the prediction result of the voltage demand change amount; Preferably, the voltage regulation margin The calculation strategy is: is the maximum adjustable voltage of the mains energy; is the output voltage of the current power supply source; Preferably, the change amount of the voltage demand of the base station electrical equipment The prediction is specifically: ; is the voltage demand change gradient of the base station electrical equipment; is the prediction time step; Preferably, when it means reaching the depletion critical point of the mains energy voltage regulation margin; S27: Trigger the energy storage device to enter the parallel power supply mode at the depletion critical point of the mains energy voltage regulation margin, and establish a cooperative working mechanism between the mains energy and the energy storage device; S28: According to the static weight of the criticality of the base station electrical equipment, initially allocate the supply voltages of the mains energy and the energy storage device under the cooperative working mechanism; Preferably, the initial allocation of the supply voltage is specifically: , ; Among them, are the initial supply voltages of the mains energy and the energy storage device respectively; is the real-time demand voltage of the electrical equipment; is the total demand voltage of the base station electrical equipment; S29: Construct a voltage distribution balance model for the energy storage device, specifically: ; Wherein, is the voltage distribution balance degree; is the rated voltage of the electrical equipment; is the actual power of the power supply; is the rated power of the base station power supply system; is the voltage matching balance weight.

[0010] It should be noted that is the voltage matching item; is the power consumption item of the power supply; It should be noted that the voltage matching item is used to ensure that the actual voltage of each electrical equipment is as close as possible to its required voltage; Specifically, in S4, the construction of the multi-objective optimization function is as follows: According to the voltage regulation efficiency, energy consumption cost and weight distribution of the power supply priority of the mains power energy and the energy storage device, construct a multi-objective optimization function, and under the constraint conditions, perform Pareto solution selection. The multi-objective optimization function is specifically: ; Wherein, are the required voltage and rated voltage of the electrical equipment respectively.

[0011] It should be noted that the multi-objective optimization function is used to minimize the voltage fluctuation of the base station electrical equipment; It should be noted that the constraint conditions include: ; ; Wherein, is the safe voltage range of the electrical equipment; are the initial supply voltages of the mains power energy and the energy storage device respectively; represents the longest switching delay of the energy storage device; is the stable time window of the target power supply; Specifically, the equal distribution scheme further includes: calculating the dynamic weight of criticality, and the dynamic weight of criticality is used to balance the voltage supply ratio of electrical equipment with different priorities during the power supply period.

[0012] Preferably, the generation method of the dynamic weight of criticality includes: ; Wherein, is the dynamic weight of criticality; It is the normal operation time of the electric equipment after the last communication failure; The longest and shortest normal operation time of the electric equipment after a communication failure; The average normal operation time of the base station electrical equipment after the last communication failure; Real-time energy efficiency ratio of electrical equipment; is the nominal energy efficiency ratio of the electrical equipment; is the average energy efficiency ratio of the base station electrical equipment; Specifically, in S5, the energy storage device management model includes: a state perception layer, the state perception layer is established based on the power supply quality evaluation index, and the perception parameters of the state perception layer include: mains power fluctuation characteristic parameters, energy storage device capacity attenuation rate and voltage deviation of base station power equipment; The power supply quality evaluation index The specific calculation strategy is: ; in, They are the mains fluctuation evaluation index, energy storage equipment attenuation evaluation index and electrical equipment voltage deviation evaluation index.

[0013] Preferably, the calculation strategy of the mains power fluctuation evaluation index is: ; in, is the mains power fluctuation rate; is the nominal voltage; Preferably, the calculation strategy of the energy storage device attenuation evaluation index is: ; Where m is the attenuation sensitivity index of the energy storage device; is the capacity decay rate of the energy storage device; Preferably, the calculation strategy of the electrical equipment voltage deviation evaluation index is specifically as follows: ; in, They are static weight of criticality and dynamic weight of criticality respectively; is the normalized deviation between the actual voltage and the required voltage of the ith electrical equipment.

[0014] Specifically, in S5, the energy storage device management model also includes: real-time monitoring of power supply quality, and when the power supply quality evaluation index recovers to a preset quality safety threshold, triggering a power supply state migration mechanism to gradually reduce the output proportion of the energy storage device.

[0015] In addition, the present invention provides a base station energy storage device management system with adaptive output voltage, including the following modules: Equipment weight determination module, demand data acquisition module, model building module, balanced allocation solution module and management and control module; The device weight determination module obtains the criticality static weight of the base station electrical equipment according to the device attributes and historical voltage demand data of the base station electrical equipment; The demand data acquisition module is used to obtain the dynamic voltage demand function curve of the base station power equipment, the energy consumption function curve of the base station power supply system and the independent conversion loss function curve of the power supply source; The model building module is used to build a voltage distribution balance model for energy storage equipment, and at the same time obtain the safe voltage range of the base station electrical equipment as a constraint condition of the voltage distribution balance model; The balanced allocation scheme module optimizes the voltage distribution balance model through a multi-objective optimization function to obtain a balanced allocation scheme; The management and control module inputs the balanced allocation plan into the energy storage device management model, generates a voltage control instruction set, and completes the time-sharing voltage control management of the energy storage device according to the voltage control instruction set.

[0016] Compared with the prior art, the technical effects of the present invention are as follows: 1. The present invention obtains the static weight of the criticality of the base station power equipment, combines it with the dynamic voltage demand function curve, etc., to construct and optimize the voltage distribution balance model, which can accurately adapt to the voltage requirements of different equipment, ensure that the actual voltage of each power equipment is as close to the required voltage as possible, ensure the stable operation of the equipment, and reduce the risk of performance degradation or failure due to voltage deviation.

[0017] 2. The present invention takes into account the voltage regulation efficiency and energy consumption cost of AC power and energy storage equipment, constructs a multi-objective optimization function for Pareto solution selection, and can effectively reduce the energy consumption of the base station power supply system. By comparing the voltage matching items and power consumption items of different allocation schemes, a lower energy consumption scheme is selected. For example, in the voltage distribution of AC power and energy storage equipment, unnecessary power consumption is reduced, saving operating costs.

[0018] 3. The present invention introduces dynamic weights of criticality, and balances the voltage supply ratios of devices with different priorities during the power supply period according to factors such as the operating time and energy efficiency ratio after a communication failure of the device, thereby achieving dynamic and flexible adjustment. At the same time, the state perception layer monitors parameters such as mains fluctuations, energy storage device attenuation, and device voltage deviation in real time based on the power supply quality evaluation index. When the power supply quality changes, the corresponding mechanism is triggered, such as the power supply source state migration mechanism, to automatically adjust the output ratio of the energy storage device to ensure stable and reliable power supply quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them: Figure 1 It is a schematic flowchart of a management method for a base station energy storage device with adaptive output voltage according to the present invention; Figure 2 It is a schematic diagram of data flow of a management method for a base station energy storage device with adaptive output voltage according to the present invention; Figure 3 It is a schematic structural diagram of a management system for a base station energy storage device with adaptive output voltage according to the present invention. Specific Embodiments

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the drawings of the specification.

[0021] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0022] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selectively exclusive embodiment from other embodiments.

[0023] Embodiment 1: As Figure 1 and Figure 2 shown, a management method for a base station energy storage device with adaptive output voltage according to an embodiment of the present invention, as Figure 1 shown, includes the following specific steps: S1: Obtain the static weight of the criticality of the base station power consumption equipment according to the equipment attributes and historical voltage demand data of the base station power consumption equipment; S1 includes: S11: Obtain the attribute list of the base station power consumption equipment, and calculate the static weight of the criticality of the base station power consumption equipment according to the historical voltage demand data of the base station power consumption equipment in different working modes; In one specific implementation manner, the calculation strategy of the static weight of the criticality is specifically: ; Among them, is the static weight of criticality; is the current harmonic distortion rate of the electrical equipment; is the heating rate inside the electrical equipment cabinet; is the service priority; It should be noted that is the adaptive coefficient; exemplarily, in this embodiment, is updated online through Kalman filtering; are respectively the real-time information throughput and the maximum information throughput of the electrical equipment; S12: Based on the dynamic voltage demand curves corresponding to each electrical equipment in the static weight of criticality of the electrical equipment, obtain the voltage sensitivity coefficient and power consumption curve of the corresponding electrical equipment.

[0024] In one specific embodiment, the calculation strategy of the voltage sensitivity coefficient of the electrical equipment is: ; among them, is the voltage sensitivity coefficient of the electrical equipment; It should be noted that The power consumption curve corresponding to the electrical equipment represents the power consumption of the electrical equipment at different operating voltages.

[0025] S2: Obtain the dynamic voltage demand function curve of the base station electrical equipment, the energy consumption function curve of the base station power supply system, and the independent conversion loss function curve of the power supply source; S21: Define the time window parameter of the target power supply of the energy storage device according to the communication guarantee level and the importance weight of the electrical equipment ; among them, represents the minimum guarantee start time of the critical electrical equipment with the highest service priority, represents the longest switching delay of the energy storage device; S22: Obtain the reference value of the dynamic voltage regulation rate according to the stable time window of the target power supply and the safe voltage range of the electrical equipment; In one specific embodiment, the stable time window of the target power supply is specifically: ; among them, is the stable time window of the target power supply; In one specific embodiment, the calculation strategy of the reference value of the dynamic voltage regulation rate is: ; Among them, is the reference value of the dynamic voltage regulation rate; is the safe voltage range of the electrical equipment; S23: Perform time differentiation on the dynamic voltage demand function of the base station electrical equipment to obtain the instantaneous voltage demand gradient of each electrical equipment. Preset the instantaneous voltage demand fluctuation threshold, and screen the time points at which the instantaneous voltage demand gradient of each electrical equipment exceeds the instantaneous voltage demand fluctuation threshold as the voltage regulation trigger signal points.

[0026] S24: According to the historical cooperative voltage regulation strategy of the mains power and energy storage equipment, the voltage regulation margin, and the corresponding energy consumption characteristics, classify the historical cooperative voltage regulation strategy of the mains power and energy storage equipment, the voltage regulation margin, and the corresponding energy consumption characteristics through the clustering algorithm (K-means) to obtain several historical operation scenarios; Exemplarily, in this embodiment, several historical operation scenarios include: classifying the historical load data into scenarios such as "stable mode" or "peak mode or night mode".

[0027] Allocate initial voltage values to several historical operation scenarios and determine the initial power supply voltage value of the mains power supply; In one specific implementation manner, Among them, the power consumption of the mains power at voltage V. It should be noted that the higher the voltage, the greater the energy consumption; is the deviation between the current voltage and the standard voltage. It should be noted that the greater the deviation, the more unstable the power supply; direction coefficient; if = 0, it means that the power supply scheme pursues power saving; if is very large, it means that the power supply scheme gives priority to ensuring power supply stability; S25: Continuously monitor the dynamic voltage demand function of the base station electrical equipment. When it is monitored that, it should be noted that when reaching the voltage regulation trigger signal point, calculate the voltage regulation urgency through the fuzzy decision layer, and determine the initial power supply scheme through the average value of the voltage regulation urgency of the base station electrical equipment; When <0.3, only fine-tune the mains power through PID control (adjust within a small range of PID control parameters), and continue to execute step S26 When 0.3 ≤ <0.7, enable the pre-synchronization of the energy storage equipment (reduce the switching delay), and at the same time execute step S28; When ≥0.7, force the cooperative voltage regulation mechanism of the mains power and energy storage equipment (sacrifice energy efficiency in exchange for stability), and at the same time execute step S28; In one specific embodiment, the calculation strategy of the voltage regulation urgency is as follows: ; wherein, is the voltage regulation urgency of the i-th electrical equipment; is the deviation value between the electricity demand and the actual power supply, is the baseline of the electricity demand deviation; Record the energy consumption evolution variable generated by the dynamic switching process and the independent conversion loss evolution variable of the power supply, and update the energy consumption function curve of the base station power supply system and the independent conversion loss function curve of the power supply.

[0028] S26: Calculate the voltage regulation margin of the mains power based on the voltage demand change gradient and the dynamic voltage regulation rate reference value, and simultaneously predict the change amount of the voltage demand of the base station electrical equipment; Select the depletion critical point according to the voltage regulation margin of the mains power and the prediction result of the change amount of the voltage demand; In one specific embodiment, the voltage regulation margin has the following calculation strategy: is the maximum adjustable voltage of the mains power (defined by the safe voltage range of the electrical equipment) is the output voltage of the current power supply; In one specific embodiment, the prediction of the change amount of the voltage demand of the base station electrical equipment is specifically as follows: ; is the voltage demand change gradient of the base station electrical equipment; is the prediction time step; In one specific embodiment, when it means that the depletion critical point of the voltage regulation margin of the mains power is reached; S27: Trigger the energy storage device to enter the parallel power supply mode at the depletion critical point of the voltage regulation margin of the mains power, and establish a cooperative working mechanism between the mains power and the energy storage device; S28: According to the static weight of the criticality of the base station electrical equipment, initially allocate the supply voltages of the mains power and the energy storage device under the cooperative working mechanism; In one specific embodiment, the initial allocation of the supply voltage is specifically as follows: , ; wherein, are respectively the initial supply voltages of the mains power and the energy storage device; is the real-time required voltage of the electrical equipment; is the total required voltage of the base station electrical equipment; S29: Construct a voltage distribution balance degree model for the energy storage device, specifically: ; wherein, is the nominal voltage of the electrical equipment; is the actual power of the power supply; is the rated power of the base station power supply system; is the voltage matching balance weight.

[0029] It should be noted that is the voltage matching item; is the power consumption item of the power supply; It should be noted that the voltage matching item is used to ensure that the actual voltage of each electrical equipment is as close as possible to its required voltage; S3: According to S2, construct a voltage distribution balance degree model for the energy storage device, and at the same time obtain the safe voltage range of the base station electrical equipment as the constraint condition of the voltage distribution balance degree model; S4: Optimize the voltage distribution balance degree model through a multi-objective optimization function to obtain an equilibrium distribution scheme; In S4, the construction of the multi-objective optimization function is specifically: According to the voltage regulation efficiency, energy consumption cost of the mains power and the energy storage device, and the weight distribution of the power supply priority of the electrical equipment, construct a multi-objective optimization function, and under the constraint conditions, perform Pareto solution selection. The multi-objective optimization function is specifically: ; wherein, are respectively the required voltage and the nominal voltage of the electrical equipment.

[0030] It should be noted that the multi-objective optimization function is used to minimize the voltage fluctuation of the base station electrical equipment; It should be noted that the constraint conditions include: ; ; Exemplarily, in this embodiment, it should be noted that the process of solving the Pareto optimal solution includes: inputting a three-dimensional optimization space (equipment energy consumption, voltage fluctuation and dynamic weight of criticality), combining the constraint conditions (stable time window of the target power supply and safe voltage range of the electrical equipment), outputting a set of non-dominated solutions (Pareto front), and obtaining an equilibrium distribution scheme.

[0031] Exemplarily, in this embodiment, a strategy for obtaining the supply voltage distribution scheme of the mains power and the energy storage device under a collaborative working mechanism is given; Exemplarily, in this embodiment, the power-consuming devices of the base station include Device A and Device B. Among them, the static weight of the criticality of Device A is 0.8, and the required voltage is 48V; the static weight of the criticality of Device B is 0.2, and the required voltage is 47V. Exemplarily, in this embodiment, the nominal voltage of the device is 48V, and the rated power of the base station power supply system is 1000W. Exemplarily, in this embodiment, the voltage matching balance weight ; Exemplarily, in this embodiment, the total required voltage of the power-consuming devices of the base station is 48V, that is ; Exemplarily, in this embodiment, the independent conversion loss function curve of the power supply is obtained by fitting the historical working logs of the power supply. According to the independent conversion loss function curve of the power supply, the non-linear loss coefficients of the voltage conversion of the mains power and the energy storage device are obtained. Exemplarily, in this embodiment, the non-linear loss coefficient of the voltage conversion of the mains power is 10, and the non-linear loss coefficient of the voltage conversion of the energy storage device is 20; Exemplarily, in this embodiment, the independent conversion loss function curve of the mains power is: ; The independent conversion loss function curve of the energy storage device is: ; It should be noted that for the mains power, the mains power is supplied through an AC-DC converter or a voltage regulator module, and its voltage conversion loss is non-linear. Its loss mainly comes from the conduction loss and switching loss of the switching device, and these losses are proportional to the square of the current, that is, the greater the voltage deviation, the square growth of the conversion loss (such as the Buck / Boost circuit); It should be noted that for the energy storage device, it is usually a lithium battery pack, and its output voltage will decrease with the depth of discharge. When the battery discharges, the internal resistance loss of the energy storage device is proportional to the square of the current. Therefore, in this embodiment, the independent conversion loss function curve of the energy storage device is approximately linear.

[0032] The voltage regulation margin of the mains power is 47.8V, specifically: ; ; Exemplarily, in this embodiment, the current allocation scheme is: 47.8V of the mains power and 0.2V of the energy storage device; Construct a voltage distribution balance degree model for the energy storage device, Calculate and obtain the voltage matching item and the power supply power consumption item of the current allocation scheme respectively; The calculation strategy of the voltage matching item includes: ; The calculation strategy of the power supply power consumption item includes: ; Obtain the voltage allocation fitness according to the voltage matching item and the power supply power consumption item of the current allocation scheme, specifically: ; Construct a multi-objective optimization function, and perform Pareto solution selection under the constraint conditions, specifically: Calculate the voltage fluctuation penalty value under the current allocation scheme; ; Exemplarily, in this embodiment, when the allocation scheme is adjusted to 47.5V of mains power energy and 0.5V of energy storage device, repeat the above steps, and synchronously calculate the voltage matching item and the power supply power consumption item of the adjusted allocation scheme. Among them, the voltage matching item of the adjusted allocation scheme is specifically: ; The power supply power consumption item of the adjusted allocation scheme is specifically: ; Obtain the voltage allocation fitness according to the voltage matching item and the power supply power consumption item of the adjusted allocation scheme, specifically: ; Synchronously calculate the voltage fluctuation penalty value under the adjusted allocation scheme; ; Exemplarily, in this embodiment, perform Pareto front analysis, as shown in the following table: According to the above table, the current scheme (solution 1, 47.8V of mains power energy and 0.2V of energy storage device) is superior to solution 2 in terms of voltage allocation fitness, power consumption and voltage fluctuation penalty value. Therefore, in this embodiment, solution 1 is the Pareto optimal solution.

[0033] S5: Input the balanced allocation scheme into the energy storage device management model, generate a set of voltage regulation instructions, and complete the time-sharing voltage regulation management of the energy storage device according to the set of voltage regulation instructions.

[0034] The balanced allocation scheme further includes: calculating the dynamic weight of criticality, and the dynamic weight of criticality is used to balance the voltage supply ratio of different priority power-consuming devices during the power supply period.

[0035] In one specific implementation manner, the generation method of the dynamic weight of criticality includes: ; Among them, is the dynamic weight of criticality; is the normal operation duration of the electrical equipment after the last communication failure; are the longest and shortest normal operation durations of the electrical equipment after the communication failure; is the average normal operation duration of the base station electrical equipment after the last communication failure; is the real-time energy efficiency ratio of the electrical equipment; is the nominal energy efficiency ratio of the electrical equipment; is the average energy efficiency ratio of the base station electrical equipment; Exemplarily, in this embodiment, it should be noted that the longer the normal operation duration of the electrical equipment after the last communication failure, the higher the reliability of the electrical equipment, the lower the probability of failure, and the higher the priority of the power supply for powering it.

[0036] In S5, the energy storage device management model includes: a state perception layer, which is established based on the power supply quality evaluation index, and the perception parameters of the state perception layer include: mains fluctuation characteristic parameters, energy storage device capacity attenuation rate, and voltage deviation degree of the base station electrical equipment; The calculation strategy of the power supply quality evaluation index is specifically: ; Wherein, are the mains fluctuation evaluation index, the energy storage device attenuation evaluation index, and the electrical equipment voltage deviation evaluation index respectively.

[0037] In one specific embodiment, the calculation strategy of the mains fluctuation evaluation index is: ; Wherein, is the mains fluctuation rate; is the nominal voltage; In one specific embodiment, the calculation strategy of the energy storage device attenuation evaluation index is: ; Wherein, m is the attenuation sensitivity index of the energy storage device; is the capacity attenuation rate of the energy storage device; Exemplarily, in this embodiment, the attenuation sensitivity index m of the energy storage device is 0.05; In one specific embodiment, the calculation strategy of the electrical equipment voltage deviation evaluation index is specifically: ; Wherein, They are static weight of criticality and dynamic weight of criticality respectively; is the normalized deviation between the actual voltage and the required voltage of the ith electrical equipment.

[0038] In S5, the energy storage device management model also includes: real-time monitoring of power supply quality, and when the power supply quality evaluation index recovers to a preset quality safety threshold, triggering a power supply state migration mechanism to gradually reduce the output proportion of the energy storage device.

[0039] Embodiment 2: like Figure 3 As shown, a base station energy storage device management system with adaptive output voltage according to an embodiment of the present invention is as follows: Figure 3 As shown, it includes the following modules: Equipment weight determination module, demand data acquisition module, model building module, balanced allocation solution module and management and control module; The device weight determination module obtains the criticality static weight of the base station electrical equipment according to the device attributes and historical voltage demand data of the base station electrical equipment; The demand data acquisition module is used to obtain the dynamic voltage demand function curve of the base station power equipment, the energy consumption function curve of the base station power supply system and the independent conversion loss function curve of the power supply source; The model building module is used to build a voltage distribution balance model for energy storage equipment, and at the same time obtain the safe voltage range of the base station electrical equipment as a constraint condition of the voltage distribution balance model; The balanced allocation scheme module optimizes the voltage distribution balance model through a multi-objective optimization function to obtain a balanced allocation scheme; The management and control module inputs the balanced allocation plan into the energy storage device management model, generates a voltage control instruction set, and completes the time-sharing voltage control management of the energy storage device according to the voltage control instruction set.

[0040] Embodiment three: This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned method for managing base station energy storage equipment with adaptive output voltage by calling the computer program stored in the memory.

[0041] The electronic device may vary significantly due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories. Among them, at least one computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement a management method for a base station energy storage device with adaptive output voltage provided by the above method embodiment. The electronic device can also include other components for implementing device functions. For example, the electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for input and output of data. This embodiment will not be elaborated here.

[0042] Embodiment 4: This embodiment provides a computer-readable storage medium with an erasable computer program stored thereon; When the computer program runs on a computer device, it causes the computer device to execute the above management method for a base station energy storage device with adaptive output voltage.

[0043] For example, the computer-readable storage medium can be a read-only memory (ROM for short), a random access memory (RAM for short), a compact disc read-only memory (CD-ROM for short), magnetic tape, floppy disk, and optical data storage device, etc.

[0044] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0045] It should be understood that determining B based on A does not mean determining B only based on A, and B can also be determined based on A and / or other information.

[0046] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0047] Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0048] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0049] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one way, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0050] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0051] In addition, in each embodiment of the present invention, each functional unit may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0052] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0053] The above has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A base station energy storage device management method with adaptive output voltage, characterized in that: include: S1: Obtain the criticality static weight of the base station power equipment according to the equipment attributes and historical voltage demand data of the base station power equipment; S2: Obtain the dynamic voltage demand function curve of the base station power equipment, the energy consumption function curve of the base station power supply system and the independent conversion loss function curve of the power supply source; S3: Based on S2, a voltage distribution balance model of the energy storage device is constructed, and at the same time, a safe voltage range of the base station electrical equipment is obtained as a constraint condition of the voltage distribution balance model; S4: Optimize the voltage distribution balance model through a multi-objective optimization function to obtain a balanced distribution plan; S5: Input the balanced allocation plan into the energy storage device management model, generate a voltage control instruction set, and complete the time-sharing voltage control management of the energy storage device according to the voltage control instruction set.

2. A base station energy storage device management method with adaptive output voltage according to claim 1, characterized in that S2 include: S21: Define the time window parameters for the target power supply of energy storage equipment according to the communication guarantee level and the importance weight of the power consumption equipment. ;in, Indicates the minimum guaranteed startup time of the key power-consuming equipment with the highest business priority. Indicates the longest switching delay of the energy storage device; S22: Obtaining a dynamic voltage regulation rate reference value according to a stable time window of target power supply and a safe voltage range of power-consuming equipment; S23: Perform time differentiation on the dynamic voltage demand function of the base station power equipment to obtain the instantaneous voltage demand gradient of each power equipment, preset the instantaneous voltage demand fluctuation threshold, and select the time point when the instantaneous voltage demand gradient of each power equipment exceeds the instantaneous voltage demand fluctuation threshold as the voltage regulation trigger signal point.

3. A base station energy storage device management method with adaptive output voltage according to claim 2, characterized in that: S2 also includes: S24: According to the historical coordinated voltage regulation strategy, voltage regulation margin and corresponding energy consumption characteristics of the mains energy source and the energy storage device, the historical coordinated voltage regulation strategy, voltage regulation margin and corresponding energy consumption characteristics of the mains energy source and the energy storage device are classified by a clustering algorithm to obtain several historical operation scenarios; Initial voltage values ​​are assigned to several historical operating scenarios, and the initial supply voltage value of the mains power supply is determined.

4. The method for managing base station energy storage equipment with adaptive output voltage according to claim 3, characterized in that: S2 also includes: S25: Continuously monitor the dynamic voltage demand function of the base station power equipment, and when the voltage regulation trigger signal point is detected, calculate the voltage regulation urgency through the fuzzy decision layer, and determine the initial power supply plan through the average value of the voltage regulation urgency of the base station power equipment; when <0.3, fine-tune the mains energy through PID control, and continue to execute step S26; When 0.3≤ <0.7, enable energy storage device pre-synchronization and execute step S28 at the same time; when ≥0.7, force the coordinated voltage regulation mechanism between the mains energy source and the energy storage device, and execute step S28 at the same time; in, To adjust the urgency of the pressure; Record the energy consumption evolution variables and the independent conversion loss evolution variables of the power supply source generated by the dynamic switching process, and update the energy consumption function curve of the base station power supply system and the independent conversion loss function curve of the power supply source; S26: Based on the voltage demand change gradient and the dynamic voltage regulation rate reference value, the voltage regulation margin of the mains energy is calculated, and the voltage demand change amount of the base station power equipment is predicted; The exhaustion critical point is selected based on the prediction results of the voltage regulation margin of the city power energy and the voltage demand change; S27: At the critical point where the voltage regulation margin of the mains energy is exhausted, the energy storage device is triggered to enter the parallel power supply mode, and a collaborative working mechanism between the mains energy and the energy storage device is established; S28: Initially allocate the supply voltage of the mains energy and the energy storage equipment under the collaborative working mechanism according to the static weight of the criticality of the base station power equipment; S29: Construct a voltage distribution balance model for energy storage equipment, specifically: ； in, Assigning balance to voltage; is the criticality static weight; The real-time required voltage of the electrical equipment; It is the nominal voltage of the electrical equipment; is the actual power of the power supply; Rated power of the power supply system for the base station; is the voltage matching balance weight, and n is the total number of electrical devices.

5. The method for managing base station energy storage equipment with adaptive output voltage according to claim 4, characterized in that: In S4, the construction of the multi-objective optimization function is specifically as follows: according to the weight distribution of the voltage regulation efficiency, energy consumption cost and power supply priority of the mains energy and energy storage equipment, a multi-objective optimization function is constructed, and under the constraints, a Pareto solution is selected. The multi-objective optimization function Specifically: ； in, is the voltage sensitivity coefficient of the electrical equipment; They are the required voltage and nominal voltage of the electrical equipment respectively.

6. A base station energy storage device management method with adaptive output voltage according to claim 5, characterized in that: The balanced allocation scheme also includes: calculating a criticality dynamic weight, where the criticality dynamic weight is used to balance the voltage supply ratio of electrical equipment with different priorities during a power supply period.

7. A base station energy storage device management method with adaptive output voltage according to claim 6, characterized in that: In S5, the energy storage device management model includes: a state perception layer, the state perception layer is established based on the power supply quality evaluation index, and the perception parameters of the state perception layer include: mains power fluctuation characteristic parameters, energy storage device capacity attenuation rate and voltage deviation of base station power equipment; The power supply quality evaluation index The specific calculation strategy is: ； in, They are the mains fluctuation evaluation index, energy storage equipment attenuation evaluation index and electrical equipment voltage deviation evaluation index.

8. A base station energy storage device management method with adaptive output voltage according to claim 7, characterized in that: In S5, the energy storage device management model also includes: real-time monitoring of power supply quality, and when the power supply quality evaluation index recovers to a preset quality safety threshold, triggering a power supply state migration mechanism to gradually reduce the output proportion of the energy storage device.

9. A base station energy storage device management system with adaptive output voltage, which is implemented based on a base station energy storage device management method with adaptive output voltage as claimed in any one of claims 1 to 8, characterized in that: The system includes the following modules: Equipment weight determination module, demand data acquisition module, model building module, balanced allocation solution module and management and control module; The device weight determination module obtains the criticality static weight of the base station electrical equipment according to the device attributes and historical voltage demand data of the base station electrical equipment; The demand data acquisition module is used to obtain the dynamic voltage demand function curve of the base station power equipment, the energy consumption function curve of the base station power supply system and the independent conversion loss function curve of the power supply source; The model building module is used to build a voltage distribution balance model for energy storage equipment, and at the same time obtain the safe voltage range of the base station electrical equipment as a constraint condition of the voltage distribution balance model; The balanced allocation scheme module optimizes the voltage distribution balance model through a multi-objective optimization function to obtain a balanced allocation scheme; The management and control module inputs the balanced allocation plan into the energy storage device management model, generates a voltage control instruction set, and completes the time-sharing voltage control management of the energy storage device according to the voltage control instruction set.

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