Power consumption management method based on composite energy supply and storage

Abstract

The invention discloses a power consumption management method based on composite energy supply and storage, and the method is used for a composite energy supply and storage system of photovoltaic, battery and power grid, and comprises the steps: carrying out the self-detection of a battery through a BMS, detecting the photovoltaic through an energy storage inverter module, and determining an operation mode, which comprises a photovoltaic power supply mode, a battery power supply mode and a power grid power supply mode; monitoring a photovoltaic voltage change rate in real time to execute switching between a photovoltaic power supply mode and a battery power supply mode; the SOC of the battery is monitored in real time, when the SOC of the battery is smaller than a first threshold value, switching from the battery power supply mode to the power grid power supply mode is executed, and the first threshold value is determined according to the battery temperature, the power consumption power of the core equipment, the weather and the power consumption time period. A photovoltaic voltage change rate is introduced as a pre-judgment basis of mode switching, a battery SOC switching threshold model based on multi-dimensional dynamic factors is constructed, and the technical problems that in an existing composite energy storage system, the power supply interruption risk is high, and battery management is extensive are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power utilization management, and particularly relates to a power utilization management method based on composite energy supply and storage. BACKGROUND

[0002] With the growing demand for clean energy worldwide, photovoltaic power generation, as a clean and renewable energy form, has been widely used in household and commercial electricity. However, due to the intermittency and instability of solar energy, it is difficult to meet the all-weather electricity demand of users simply relying on photovoltaic systems. Therefore, the composite energy supply and storage system emerges as the times require. The system integrates photovoltaic arrays, energy storage batteries and grid interfaces, and realizes efficient switching and collaborative work between multiple energy sources through intelligent control strategies to ensure stable power supply for users at any time.

[0003] Early household energy storage systems mainly rely on simple grid-connected inverters or off-grid inverters, which have relatively single functions and cannot automatically adjust the operation mode according to real-time environmental conditions. With the development of technology, energy storage inverters with basic BMS (Battery Management System) and MPPT (Maximum Power Point Tracking) functions appear, which can optimize photovoltaic power generation efficiency and battery charge and discharge management to a certain extent. However, these systems still lack intelligent energy scheduling mechanisms, switching delays and voltage fluctuations, and unreasonable mode switching threshold settings.

[0004] Although the existing composite energy storage system solves the intermittency problem of photovoltaic power generation to a certain extent, in actual application, when the light conditions change rapidly (such as cloud cover), the system cannot respond in time, resulting in power interruption or voltage fluctuation.

[0005] Most of the existing systems use fixed thresholds to determine when to switch from battery power to grid power. However, the performance of the battery changes under different environmental conditions, for example, in low temperature environment, the battery capacity will decrease significantly, if the fixed threshold is continued to be used, it may cause the battery to be depleted prematurely, affecting the continuous power supply of critical loads.

[0006] Therefore, it is urgent to develop a power utilization management method based on composite energy supply and storage to solve the problems of switching delay and unstable power supply in the existing composite energy storage system. SUMMARY

[0007] The application provides a power utilization management method based on composite energy supply and storage, which solves the problems of switching delay and unstable power supply in the existing composite energy storage system.

[0008] The technical scheme adopted by the present application is: A power utilization management method based on composite energy supply and storage for a composite energy supply and storage system including photovoltaic, battery and grid, the method comprising: performing battery self-checking through the BMS and detecting the photovoltaic through the energy storage inverter module to determine an operation mode, wherein the operation mode comprises a photovoltaic power supply mode, a battery power supply mode and a grid power supply mode; real-time monitoring of a photovoltaic voltage change rate to perform switching between the photovoltaic power supply mode and the battery power supply mode; real-time monitoring of a battery SOC, and performing switching from the battery power supply mode to the grid power supply mode when the battery SOC is less than a first threshold value, wherein the first threshold value is determined according to a battery temperature, a core device power consumption, weather and a power consumption time period.

[0009] The power consumption management method based on composite power storage energy of the application further has the following additional technical features: the photovoltaic power supply mode, the battery power supply mode and the grid power supply mode, in particular: in the photovoltaic power supply mode, directly supplying power through the photovoltaic, when the photovoltaic power supply power is greater than the current power consumption, supplying power to the battery or the grid through the photovoltaic, wherein switching between supplying power to the battery or the grid is realized according to the battery SOC; in the battery power supply mode, directly supplying power through the battery, when the battery SOC is less than a second threshold value, performing charging to the battery through the photovoltaic; in the grid power supply mode, directly supplying power through the grid.

[0010] performing battery self-checking through the BMS and detecting the photovoltaic through the energy storage inverter module, in particular: performing voltage and short-circuit current tests on the photovoltaic to determine weather through voltage and to determine power supply power through voltage and short-circuit current; performing voltage, SOC, temperature and insulation impedance tests on the battery to determine battery abnormalities through voltage, temperature and insulation impedance tests.

[0011] determining an operation mode, in particular: if the photovoltaic power supply power is greater than or equal to the current power consumption, entering the photovoltaic power supply mode; otherwise, when the battery is not abnormal, entering the battery power supply mode.

[0012] real-time monitoring of a photovoltaic voltage change rate to perform switching between the photovoltaic power supply mode and the battery power supply mode, in particular: when the photovoltaic voltage change rate is less than 0.5 V / s, maintaining the photovoltaic power supply mode; When the photovoltaic voltage variation rate is greater than or equal to 0.5V / s and less than or equal to 5V / s, pre-discharge of the photovoltaic or battery is performed by the energy storage inverter to realize voltage pre-synchronization for switching transition between the photovoltaic power supply mode and the battery power supply mode. When the photovoltaic voltage variation rate is greater than 5V / s, the photovoltaic power supply mode is switched to the battery power supply mode.

[0013] The first threshold is determined according to the battery temperature, the core device power consumption, the weather and the power consumption period, and specifically: The first threshold is preset to be 20%, According to the battery temperature, the core device power consumption, the weather and the power consumption period, a correction coefficient is set respectively to adjust the preset first threshold to determine the final first threshold, The correction coefficient corresponding to the battery temperature is positively correlated with the battery temperature, and the correction coefficient corresponding to the core device power consumption is positively correlated with the core device power consumption.

[0014] The weather specifically includes: The weather is determined by the photovoltaic voltage, when the photovoltaic voltage peak is greater than 300V, it is determined to be sunny, and the correction coefficient is set to 0.8; When the photovoltaic voltage peak is less than 200V, it is determined to be rainy, and the correction coefficient is set to 1.3; When the photovoltaic voltage peak is greater than or equal to 200V and less than or equal to 300V, it is determined to be cloudy, and the correction coefficient is set to 1.

[0015] The power consumption period specifically includes: According to the power consumption statistics in the historical time interval, the valley power consumption period and the peak power consumption period are divided, In the valley power consumption period, the correction coefficient is set to 0.7, In the peak power consumption period, the correction coefficient is set to 1.4.

[0016] The application further provides a storage medium, The storage medium stores a computer program, and the computer program is executed to realize the steps of the power consumption management method based on the composite energy supply and storage.

[0017] The application further provides a processing device, which includes: A memory for storing a computer program; A processor for executing the computer program to realize the steps of the power consumption management method based on the composite energy supply and storage.

[0018] Thanks to the above technical solutions, the application has the following beneficial effects: 1. In the present application, by monitoring the photovoltaic voltage rate of change (dV / dt) in real time, the deterioration trend of the light condition can be predicted before the photovoltaic output power has not yet significantly decreased. Based on this prediction, the preparation of the battery power supply mode can be started in advance, realizing feedforward control instead of feedback control after the failure occurs. The voltage drop and power supply interruption caused by the sudden change of photovoltaic output can be effectively avoided, realizing smooth and seamless switching between photovoltaic and battery power supply modes, and significantly improving the power supply continuity and voltage stability of the key load.

[0019] In addition, by setting the first threshold value as a variable dynamically adjusted according to the battery temperature, the core device power consumption, the weather, and the power consumption period, the system realizes the adaptability and intelligence of battery power management. The switching threshold is automatically increased at low temperature to prevent over-discharge of the battery in the capacity attenuation state. The threshold is increased when the core device power is large to ensure that there is enough power to support high load operation. The threshold is increased when it is identified as a rainy day to reserve more power for unexpected situations; the threshold is decreased on sunny days to fully utilize photovoltaic resources. In this way, over-discharge and premature depletion of the battery are avoided, the battery life is extended, and accurate power management is achieved.

[0020] Moreover, the battery self-checking / photovoltaic detection (basic state perception), photovoltaic voltage rate monitoring (dynamic process prediction), and SOC dynamic threshold switching (intelligent decision-making) form a complete closed-loop control chain from perception to decision-making to execution. Instead of being a passive response to failure, it has the ability to predict, judge, and adapt, making the overall control strategy more intelligent, efficient, and robust, significantly improving the autonomous operation level and user experience of the composite energy storage system.

[0021] In summary, by introducing the photovoltaic voltage rate as a prediction basis for mode switching, and constructing a battery SOC switching threshold model based on multiple dynamic factors, the key technical problems of high power supply interruption risk, extensive battery management, and poor operation economy in existing composite energy storage systems are effectively solved, the power supply continuity is significantly enhanced, and the intelligence level of battery management is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions serve to explain the present application, and do not constitute an improper limitation of the present application. In the drawings: Figure 1 The figure is a flowchart of the power management method based on composite energy storage according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to more clearly explain the overall concept of the present application, the following detailed description is given with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0025] like Figure 1 As shown, a power management method based on hybrid energy supply and storage is used in a hybrid energy supply and storage system including photovoltaics, batteries, and the power grid. The method includes: S100: Perform battery self-test via BMS, detect photovoltaic through energy storage inverter module, and determine operating mode, wherein the operating mode includes photovoltaic power supply mode, battery power supply mode, and grid power supply mode.

[0026] The core purpose of this step is to complete the health status and availability assessment of the core components (batteries, photovoltaics) of the energy storage system before the system is powered on or the mode is switched, and to autonomously determine the optimal initial operating mode based on the assessment results, so as to ensure the safe start-up of the system, assess the availability of energy sources, and realize autonomous decision-making on the operating mode.

[0027] This step relies on the coordinated operation of two core components: the Battery Management System (BMS) and the energy storage inverter module. Specifically, the BMS performs a battery self-test and generates a battery status report, including status codes such as normal and abnormal. The energy storage inverter module detects the photovoltaic system, generates a photovoltaic status report, and determines the photovoltaic power output.

[0028] The central control unit (MCU) receives detection reports from the BMS and inverter, performs mode determination based on preset logic, and determines the operating mode, including photovoltaic power supply mode, battery power supply mode, and grid power supply mode.

[0029] Understandably, if the photovoltaic panels are shaded or damaged, the system should not blindly switch to photovoltaic power supply mode; if the batteries are malfunctioning, it should not attempt battery power supply mode. This step ensures the system selects a truly usable energy source, avoiding control logic failure. It ensures the system always operates under the premise that the components are in good condition, avoiding system downtime or performance degradation due to component failure, thus improving overall operational stability and reliability. Through pre-checking, the system can directly enter the optimal mode, without going through the inefficient trial-failure-switch process, shortening startup time and improving response speed.

[0030] In summary, this step, as the starting point and foundation of the power management method for the composite energy storage system, achieves a comprehensive state assessment and autonomous determination of the optimal operating mode before system startup through a three-step strategy of BMS battery self-test + inverter photovoltaic detection + intelligent mode decision-making.

[0031] S200: Monitors the photovoltaic voltage change rate in real time to perform switching between the photovoltaic power supply mode and the battery power supply mode.

[0032] The core purpose of this step is to realize early prediction of sudden change of light conditions by monitoring the rate of change of photovoltaic voltage in real time, and to actively perform smooth switching between photovoltaic power supply mode and battery power supply mode, avoid load voltage interruption or fluctuation caused by photovoltaic output drop, and ensure the continuous and stable operation of sensitive electrical equipment. Specifically, the feedforward control of the power supply mode is realized, the power supply continuity is improved, and the smoothness of the switching process is optimized.

[0033] Real-time acquisition of photovoltaic voltage, through the built-in high-precision ADC (analog-to-digital converter) in the energy storage inverter module, the direct current voltage (V_pv) of the photovoltaic input end is continuously collected at a high sampling frequency (such as 1 kHz or higher). In addition, data preprocessing is performed, and the original voltage data is filtered (such as sliding average filtering, Kalman filtering) to eliminate high-frequency noise and interference caused by MPPT disturbance, and ensure the accuracy of dV / dt calculation.

[0034] Calculate the rate of change of photovoltaic voltage (dV / dt), dV / dt=(V t -V t-1 ) / Δt, where V t is the photovoltaic voltage collected at the current time; V t-1 is the photovoltaic voltage collected at the last time; and Δt is the sampling period (such as 1 ms). dV / dt is calculated every 1 ms to form a continuous rate sequence.

[0035] It should be noted that when the rate of change of photovoltaic voltage is small, the photovoltaic executes stable power supply, and maintains the photovoltaic power supply mode. When the rate of change of photovoltaic voltage is small, it indicates that the photovoltaic voltage has a rapid decay trend. In order to avoid the photovoltaic voltage falling below the allowable voltage and affecting the stability of power supply, the mode switching is executed, and the battery power supply mode is switched to.

[0036] It should be noted that in order to cope with the intermittency of photovoltaic output, the solar energy is greatly affected by the weather, and the light intensity can change dramatically within a few seconds. The traditional reactive switching based on power threshold has detection delay and control delay, and it is difficult to respond to rapid changes.

[0037] In addition, to avoid voltage drop, when the photovoltaic output is instantaneously zero, if the system cannot respond in time, the output voltage will rapidly drop due to the load demand, causing sensitive equipment to restart or damage. This step uses dV / dt prediction to advance control and realize seamless switching of power supply, fundamentally solving this problem and ensuring uninterrupted voltage on the user side, effectively avoiding load restart.

[0038] In summary, the present step realizes early prediction of light mutation and feedforward control of power supply mode by monitoring photovoltaic voltage change rate in real time, effectively solves the key technical problems of switching delay, voltage interruption and other key technical problems of traditional composite energy storage system in response to rapid weather changes, thereby ensuring stable operation of critical loads and realizing power supply continuity and power quality.

[0039] S300: Real-time monitoring of battery SOC, when the battery SOC is less than the first threshold value, the switching of the battery power supply mode to the grid power supply mode is performed, wherein the first threshold value is determined according to battery temperature, core device power consumption, weather, and power consumption period.

[0040] The core purpose of the present step is to break through the limitations of fixed SOC threshold switching in traditional energy storage systems, and by constructing an adaptive threshold model based on multi-dimensional dynamic factors (battery temperature, core device power consumption, weather, and power consumption period), the intelligentization and refinement of battery power supply switching decision are realized, thereby prolonging the battery life and improving the overall energy utilization efficiency under the premise of ensuring continuous power supply of critical loads.

[0041] Real-time monitoring of battery SOC, BMS (battery management system) calculates and reports the current battery state of charge (SOC) in real time by open circuit voltage method (OCV-SOC) combined with Coulomb counting. It should be noted that the SOC estimation error should be controlled within ±3% to ensure the accuracy of the decision.

[0042] In addition, the battery temperature (T_bat) is collected by the built-in temperature sensor of the BMS to obtain the average temperature or the highest temperature of the battery module. The core device power consumption (P_load) is identified and monitored by the intelligent power meter or the output side power sensor of the inverter to identify and monitor the real-time power of the critical load (such as refrigerator, air conditioner, server).

[0043] For weather conditions, the following methods can be used: Local identification, based on photovoltaic voltage characteristics (such as daily peak voltage, fluctuation rate) for judgment; External acquisition, weather forecast API data is obtained through Wi-Fi / 4G module. The present application does not limit this.

[0044] Power consumption period (Time_Slot), determined by the built-in RTC (real-time clock) of the system combined with the preset peak-valley electricity price period table to determine whether the current belongs to low valley, flat section or peak power consumption period.

[0045] It can be understood that the traditional system adopts a fixed SOC threshold (such as 20%), which has obvious disadvantages, for example, the actual available capacity of 20% SOC at low temperature may be insufficient, resulting in over-discharge; 20% of the power is still reserved on sunny days, which wastes the photovoltaic charging opportunity.

[0046] The application determines the first threshold value by battery temperature, core device power consumption, weather, and power consumption period, prevents over-discharge at low temperature through temperature compensation, avoids deep discharge through weather prediction, effectively slows down battery aging, and prolongs its cycle life. In the risk scenarios of rainy days, high loads, etc., the switching threshold is automatically increased to reserve more power, effectively preventing the key load from being powered off due to power consumption. Allow deeper discharge on sunny days to maximize the use of photovoltaic resources; avoid reserving too much power on chargeable days to improve the overall energy efficiency of the system.

[0047] In summary, the first threshold value is determined based on multiple dynamic factors in this step, which realizes the intelligentization and safety of the switching decision of battery power supply to grid power supply, solves the technical problems of resource waste, cost increase, power failure risk and other technical problems caused by traditional fixed threshold value, and realizes the prolongation of battery life, optimization of electricity cost, and improvement of reliability.

[0048] As a preferred embodiment of the application, the photovoltaic power supply mode, the battery power supply mode, and the grid power supply mode are specifically: In the photovoltaic power supply mode, direct power supply is performed through photovoltaic, When the photovoltaic power supply power is greater than the current power consumption, power supply is performed to the battery or the grid through photovoltaic, wherein the switching between power supply to the battery and power supply to the grid is realized according to the battery SOC; In the battery power supply mode, direct power supply is performed through the battery, When the battery SOC is less than the second threshold value, charging is performed to the battery through photovoltaic; In the grid power supply mode, direct power supply is performed through the grid.

[0049] The core purpose of this preferred embodiment is to ensure that in the three basic running states of photovoltaic power supply mode, battery power supply mode, and grid power supply mode, the system can maximize the use of renewable energy, reasonably manage energy storage, and realize intelligent interaction with the grid, thereby improving the overall energy utilization efficiency and system economy.

[0050] In the photovoltaic power supply mode, photovoltaic is the first energy source, and the load demand is preferentially met. The photovoltaic array directly supplies power to the local load through the energy storage inverter. At this time, when the photovoltaic output power > local load power, excess energy is generated.

[0051] According to the battery SOC, if the battery SOC < second threshold (such as 90%) → start photovoltaic charging to the battery (MPPT charging). If the battery SOC ≥ second threshold → start photovoltaic to the grid (grid-connected inverter). Introducing the battery SOC as the switching criterion of charging / feeding to the grid, avoiding overcharging of the battery, and realizing the orderly distribution of energy.

[0052] In the battery power supply mode, the battery is the first energy source, and undertakes the power supply task when the photovoltaic is insufficient. The energy storage battery provides alternating current for the local load through the inverter. The battery SOC is monitored in real time, and when the battery SOC < first threshold (such as 30%), the system tries to restore photovoltaic charging.

[0053] The charging energy source is performed by photovoltaic. If the current light is sufficient, the photovoltaic directly charges the battery. If the light is insufficient, the battery power supply is maintained, or the grid power supply is switched according to the overall strategy.

[0054] It should be noted that in this mode, although the photovoltaic cannot meet the power supply demand, it still generates part of the electricity, which is directly charged into the battery to maintain the service life of the electricity in the battery. It should be noted that the battery is not charged in this mode. Through the grid, so as to avoid the energy loss of the battery conversion. It is emphasized that in the battery power supply mode, the photovoltaic resource is used to supplement the electricity of the battery, realizes the cooperation of photovoltaic and storage, and maximizes the utilization rate of renewable energy.

[0055] In the grid power supply mode, the grid is the only or main energy source. The mains directly supplies power to the load through the inverter or bypass.

[0056] Maximizing the utilization rate of renewable energy, through the strategies of photovoltaic priority power supply, intelligent distribution of excess electricity (charging / feeding to the grid), and photovoltaic power supply in the battery mode, it is ensured that the photovoltaic energy is fully and efficiently utilized, and the light is reduced. The excess electricity feeding to the grid can create additional benefits (such as electricity bill deduction) for users; the SOC-oriented charging strategy avoids overcharging of the battery, prolongs its service life, and reduces replacement cost.

[0057] In summary, the preferred embodiment defines the specific energy flow and control logic of the three power supply modes of photovoltaic, battery and grid, introduces the excess electricity distribution mechanism based on SOC and the strategy of photovoltaic active power supply in the battery mode, and significantly improves the energy utilization efficiency, economy and intelligent level of the system.

[0058] As a preferred embodiment of the present application, the battery is self-checked by the BMS, and the photovoltaic is detected by the energy storage inverter module, specifically: The voltage and short-circuit current of the photovoltaic are tested to determine the weather by voltage and determine the power supply power by voltage and short-circuit current; The voltage, SOC, temperature, and insulation impedance of the battery are tested to determine the battery abnormalities through voltage, temperature, and insulation impedance tests.

[0059] The core purpose of this preferred embodiment is to equip the battery self-check and photovoltaic detection with environmental perception and fault diagnosis capabilities. By performing specific electrical parameter tests (voltage, short-circuit current, SOC, temperature, and insulation impedance), not only the health status of the assembly is evaluated, but also the external environment (weather) and energy potential (power supply power) are inferred, thereby providing more abundant and intelligent input basis for subsequent operation mode decision-making.

[0060] For photovoltaic tests, open-circuit voltage (Voc), before MPPT starts, disconnect the load and measure the open-circuit voltage of the photovoltaic array. Short-circuit current (Isc), under safe conditions, briefly connect the short-circuit loop and measure the short-circuit current of the photovoltaic array (modern inverters usually estimate through MPPT scanning to avoid real short-circuit).

[0061] Determine the weather by voltage (Voc). The open-circuit voltage (Voc) of the photovoltaic assembly is greatly affected by temperature, but relatively less affected by light intensity. While light intensity directly affects short-circuit current (Isc). Therefore, after knowing the component temperature or performing temperature compensation, the size of Voc can reflect whether the component is shaded or in a weak light environment.

[0062] Determine the power supply power by voltage (Voc) and short-circuit current (Isc). The maximum theoretical output power of the photovoltaic array P_max≈Voc×Isc×FF (FF is the fill factor, which can be pre-set or estimated). Real-time measure or estimate Voc and Isc. Calculate P_max_estimate=k×Voc×Isc (k is an empirical coefficient, including FF and efficiency). Take P_max_estimate as the estimated value of the current photovoltaic maximum available power, which is used to determine whether it is sufficient to support the load or charge the battery. If P_max_estimate>load power, then prefer photovoltaic power supply mode.

[0063] For battery tests, voltage, measure the total voltage of the battery pack and the voltage of each single cell. SOC, estimated by BMS through OCV method, coulomb integration method, etc. Temperature, measured by NTC or PT100 sensors placed at key positions of the battery module. Insulation impedance (Insulation Resistance, IR), through the insulation detection module (IMD), apply a test voltage to the positive and negative electrodes of the battery, measure the leakage current to ground, and calculate the insulation impedance value.

[0064] The BMS compares the above parameters with the preset threshold values to generate a "battery status code", such as normal, abnormal, etc. Specifically, abnormal can be divided into overvoltage, undervoltage, overtemperature, low temperature, insulation fault, low SOC, etc.

[0065] The maximum photovoltaic output can be estimated before starting, providing a quantitative basis for mode selection and avoiding frequent mode switching or power shortage caused by power misjudgment. Through the fourfold protection of voltage, temperature, SOC, and insulation impedance, various abnormalities of the battery can be found comprehensively and timely, effectively preventing overcharging, overdischarging, overheating, and leakage, and significantly improving system safety. Timely detection of single cell pressure difference and temperature abnormalities can trigger maintenance actions such as balancing and heat dissipation, slowing down battery aging and prolonging its cycle life.

[0066] In summary, the preferred embodiment defines photovoltaic detection and battery self-checking, uses photovoltaic Voc to judge the weather, uses Voc / Isc to estimate power, and performs multi-dimensional health diagnosis on the battery including insulation impedance, significantly improving the system's state perception ability, environmental adaptability, and intrinsic safety level.

[0067] As a preferred embodiment of the present embodiment, the running mode is determined, specifically: If the photovoltaic power supply power is greater than or equal to the current power consumption, enter the photovoltaic power supply mode; Otherwise, when the battery is not abnormal, enter the battery power supply mode.

[0068] The core purpose of this embodiment is to provide a simple, efficient, and logically clear running mode determination rule. By directly comparing the photovoltaic power supply power with the current power consumption and combining the basic health status of the battery, the system can quickly and reliably make decisions in the three power supply modes, maximizing the use of renewable energy while ensuring seamless power supply by the energy storage system when photovoltaic power is insufficient.

[0069] First priority photovoltaic power supply, when P_pv≥P_load, regardless of the battery state, photovoltaic power supply is preferred. At this time, the excess photovoltaic energy can be used for battery charging or grid feeding according to the preferred embodiment described above.

[0070] Second priority battery power supply, when P_pv<P_load (i.e. insufficient photovoltaic output), the system checks whether the battery is "non-abnormal". If the battery is healthy, the battery is used as a supplementary or main power source, and the battery power supply mode is entered.

[0071] Third priority grid power supply, if the photovoltaic power is insufficient and the battery is abnormal (such as failure, overdischarge, overtemperature, etc.), the system finally enters the grid power supply mode to ensure uninterrupted power supply for users.

[0072] As long as there is enough sunlight, photovoltaic power is used directly, reducing dependence on the power grid and electricity bills, in line with the core goal of energy saving and emission reduction. The battery is specifically positioned as a supplement to photovoltaic and a replacement for the power grid, providing backup power when photovoltaic is insufficient, avoiding overuse or waste of the battery.

[0073] Through battery non-abnormality inspection, the reliability of the backup power supply is ensured, avoiding the embarrassing situation of decision switching but power supply unavailable, and guaranteeing the reliability of power supply.

[0074] In summary, the preferred embodiment establishes clear energy utilization priorities through the simple and efficient decision logic of "photovoltaic power supply power ≥ current power consumption → photovoltaic power supply mode; otherwise and battery non-abnormality → battery power supply mode", realizing fast and reliable decision of system operation mode. Its setting not only ensures the maximum utilization of renewable energy, simplifies the control complexity, but also guarantees the reliability of backup power supply through "battery health check", which is the core control strategy for the "intelligent, economic and reliable" operation of the composite energy supply and storage system, with outstanding practicality and market value.

[0075] As a preferred embodiment of the present application, the photovoltaic voltage rate of change is monitored in real time to perform switching between the photovoltaic power supply mode and the battery power supply mode, specifically: When the photovoltaic voltage rate of change is <0.5V / s, the photovoltaic power supply mode is maintained; When the photovoltaic voltage rate of change is ≥0.5V / s and ≤5V / s, pre-discharge of photovoltaic or battery is performed through the energy storage inverter to realize voltage pre-synchronization for switching transition between the photovoltaic power supply mode and the battery power supply mode; When the photovoltaic voltage rate of change is >5V / s, switching from the photovoltaic power supply mode to the battery power supply mode is performed.

[0076] The core purpose of this preferred embodiment is to break through the limitations of traditional reactive switching (such as acting only when the power drops below a threshold), by introducing the photovoltaic voltage rate of change (dV / dt) as a trend parameter and setting a multi-level response mechanism (maintenance, pre-synchronization, direct switching), to realize early prediction and graded response to sudden changes in light, thereby optimizing the smoothness of the switching process and power quality under the premise of ensuring power continuity, and improving the user's power experience.

[0077] According to the calculated dV / dt value, the system performs different control actions: dV / dt<0.5V / s→ maintain the current mode, photovoltaic voltage is stable or slowly decreasing, light conditions are good or only slightly fluctuating. The system determines that it is in a normal state, continues to maintain the photovoltaic power supply mode, and does not perform any switching operation. This can avoid unnecessary switching caused by minor fluctuations and ensure the stability of system operation.

[0078] 0.5V / s ≤ dV / dt ≤ 5V / s → Pre-synchronization preparation is initiated. The photovoltaic voltage is decreasing at a moderate rate, indicating that the sunlight may continue to weaken (e.g., from cloudy to overcast), but it has not yet reached an emergency level. Pre-discharge is initiated. The MCU sends a command to the energy storage inverter to start the battery-side inverter module, putting it into standby power generation mode and outputting an open-circuit voltage that is in phase and frequency with the grid / load voltage. The inverter continuously adjusts the amplitude, frequency, and phase of its output voltage to keep it consistent with the output voltage (or load voltage) of the current photovoltaic power supply circuit. The battery system enters a power-supplying state, and the power devices preheat, preparing for a possible switchover.

[0079] This buys time for a seamless transition. If dV / dt continues to increase or the photovoltaic power is indeed insufficient, the transition can be performed immediately. Because pre-synchronization has been completed, the transition process will be extremely smooth, without voltage dips or flicker.

[0080] When dV / dt > 5V / s, an immediate mode switch is executed. The photovoltaic voltage drops sharply, indicating a sudden change in sunlight (such as rapid cloud cover blocking the sun). The photovoltaic output will significantly decrease or drop to zero within a very short time. The MCU issues a hard switch command, controlling the inverter to quickly disconnect the photovoltaic input circuit. Simultaneously, the battery power supply circuit, which is already in pre-synchronization or standby mode, is connected to the load.

[0081] Responding to emergencies as quickly as possible and ensuring uninterrupted power supply to the user side is the last line of defense for ensuring the uninterrupted operation of critical loads such as servers and medical equipment.

[0082] With pre-synchronization preparation, the system can complete power switching within microseconds to milliseconds, ensuring uninterrupted and stable voltage on the user side and maximizing power continuity. The switching process is smooth, avoiding power quality issues such as voltage dips, flicker, and frequency fluctuations, providing high-quality power to sensitive electronic equipment. This upgrade from passive response to proactive prediction reflects the intelligence of the control strategy.

[0083] The graded response mechanism ensures that the system can take the most appropriate measures under different degrees of light change, neither overreacting nor underreacting.

[0084] In a preferred embodiment of the present invention, the first threshold is determined based on battery temperature, power consumption of core equipment, weather, and power consumption period, specifically: The first threshold is preset to 20%. Based on battery temperature, core equipment power consumption, weather conditions, and power consumption periods, correction coefficients are set to adjust the preset first threshold, thereby determining the final first threshold. The correction coefficient corresponding to the battery temperature is positively correlated with the battery temperature, and the correction coefficient corresponding to the core device power consumption is positively correlated with the core device power consumption.

[0085] The core purpose of the preferred embodiment is to make the first threshold value, which is a key parameter for determining when the battery intervenes in power supply, an intelligent variable that can be dynamically adjusted according to multi-dimensional factors such as environment, load and time, so that the system can adapt to complex and variable actual operation scenarios and realize more refined and intelligent energy management.

[0086] The basic threshold value is set, and a default SOC switching threshold value suitable for general working conditions is set, for example, SOC_base=20%. As a reference point for dynamic adjustment, it is ensured that the system can still operate normally when there is no other information input.

[0087] The correction coefficient (K_temp) corresponding to the battery temperature is positively correlated with the battery temperature: the higher the temperature, the larger K_temp.

[0088] Specifically, if T_bat<0°C (low temperature) → K_temp=0.8 (lower threshold, use battery earlier to avoid low-temperature discharge damage); if 0°C≤T_bat≤45°C (normal temperature) → K_temp=1.0; if T_bat>45°C (high temperature) → K_temp=1.2 (increase threshold, use battery earlier to avoid photovoltaic fluctuations leading to battery overcharge or overheating).

[0089] The correction coefficient (K_power) corresponding to the core device power consumption is positively correlated with the core device power consumption: the higher the power, the larger K_power.

[0090] If P_critical<1kW → K_power=0.9; if 1kW≤P_critical≤3kW → K_power=1.0; if P_critical>3kW (such as air conditioner, electric water heater starting) → K_power=1.3 (significantly increase threshold, use battery to supply power in advance to share load and prevent photovoltaic power supply from being insufficient to cause voltage drop), to ensure the stability of power supply when high-power load starts.

[0091] The correction coefficient (K_weather) corresponding to the weather is as follows: Determine the weather by photovoltaic voltage. When the photovoltaic voltage peak is >300V, it is determined to be sunny, and the correction coefficient is set to 0.8, the light is stable, and the photovoltaic is reliable; When the photovoltaic voltage peak is <200V, it is determined to be rainy, and the correction coefficient is set to 1.3, and the photovoltaic output is extremely unstable, and the battery is used earlier; When the photovoltaic voltage peak value is ≥ 200V, ≤ 300V, it is judged as cloudy, and the corresponding correction coefficient is set to 1.

[0092] The correction coefficient (K_time) corresponding to the power consumption period is specifically: According to the power consumption statistics in the historical time interval, the low valley power consumption period and the peak power consumption period are divided, In the low valley power consumption period, the corresponding correction coefficient is set to 0.7, the threshold value is reduced, and the battery is used as much as possible, In the peak power consumption period, the corresponding correction coefficient is set to 1.4, the threshold value is greatly increased, the battery is preferentially used for power supply, the battery power is avoided to not meet the large-scale power consumption demand, and power fluctuation is avoided.

[0093] The final first threshold value is calculated, the basic value is 20%, the high temperature K_temp=1.2, the core device high power K_power=1.3, the cloudy day (K_weather=1.3), and the peak time (K_time=1.4). The final threshold value is 20%×1.2×1.3×1.3×1.4≈56.8%.

[0094] In this composite working condition, when the battery SOC is higher than 56.8%, the system can be switched to the battery power supply mode according to the photovoltaic condition, so as to guarantee power supply and save electricity charges.

[0095] The intelligent level of the system is significantly improved. The system can dynamically adjust the control strategy according to the context information such as environment, load, time and the like, and realizes real situational awareness and adaptive control. When the core device is in high-power operation or the weather is bad, the battery power supply is started in advance, the fluctuation of photovoltaic output is effectively suppressed, and the voltage stability of the key load is guaranteed. Through temperature correction, the abuse of the battery in adverse working conditions is avoided, which helps to maintain the performance of the battery and prolong the service life of the battery.

[0096] In summary, the preferred embodiment proposes a dynamic correction model based on the first threshold value, which is based on multiple factors such as battery temperature, core device power consumption, weather, power consumption period and the like. The energy storage system can adapt to complex internal and external environments, and realizes the optimal balance between power supply reliability, economy, safety and battery life.

[0097] The application also provides a storage medium, The storage medium stores a computer program, and the computer program is executed to realize the steps of the power consumption management method based on the composite energy supply and storage.

[0098] Therefore, any effect of the power consumption management method based on the composite energy supply and storage can be realized, and details are not repeated here.

[0099] The application further provides a processing device, comprising: a memory for storing a computer program; a processor for implementing the steps of the power consumption management method based on composite energy supply and storage when executing the computer program.

[0100] Therefore, any effect of the power consumption management method based on composite energy supply and storage can be achieved, which will not be described here.

[0101] The places not described in the present application can be realized by using or referring to the existing technology.

[0102] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

[0103] The above only describes the embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A power consumption management method based on composite energy supply and storage, characterized in that, The application relates to a composite power supply and storage system comprising photovoltaics, batteries and a power grid, and a method for the same, the method comprising: carrying out battery self-checking through a BMS and detecting photovoltaics through a storage inverter module to determine an operation mode, wherein the operation mode comprises a photovoltaic power supply mode, a battery power supply mode and a power grid power supply mode; real-time monitoring of a photovoltaic voltage change rate to perform switching between the photovoltaic power supply mode and the battery power supply mode; real-time monitoring of a battery SOC, and performing switching from the battery power supply mode to the power grid power supply mode when the battery SOC is less than a first threshold value, wherein the first threshold value is determined according to a battery temperature, a core device power consumption, weather and a power consumption period.

2. The method for electricity consumption management based on composite energy supply and storage according to claim 1, characterized in that, The photovoltaic power supply mode, the battery power supply mode and the power grid power supply mode are specifically: in the photovoltaic power supply mode, direct power supply is performed through photovoltaics, when photovoltaic power supply power is greater than current power consumption, power supply is performed to the battery or the power grid through photovoltaics, wherein switching between power supply to the battery or the power grid is realized according to a battery SOC; in the battery power supply mode, direct power supply is performed through the battery, when the battery SOC is less than a second threshold value, charging is performed to the battery through photovoltaics; in the power grid power supply mode, direct power supply is performed through the power grid.

3. The method for electricity consumption management based on composite energy supply and storage according to claim 1, characterized in that, The battery self-checking through the BMS and the photovoltaic detection through the storage inverter module are specifically: voltage and short-circuit current tests are performed on the photovoltaics to judge weather through voltage and to judge power supply power through voltage and short-circuit current; voltage, SOC, temperature and insulation impedance tests are performed on the battery to judge battery abnormalities through voltage, temperature and insulation impedance tests.

4. The method according to claim 3, wherein, The operation mode is determined, and the determination is specifically: if photovoltaic power supply power is greater than or equal to current power consumption, the photovoltaic power supply mode is entered; otherwise, when the battery is not abnormal, the battery power supply mode is entered.

5. The method for electricity consumption management based on composite energy supply and storage according to claim 1, characterized in that, The real-time monitoring of the photovoltaic voltage change rate to perform switching between the photovoltaic power supply mode and the battery power supply mode is specifically: when the photovoltaic voltage change rate is less than 0.5 V / s, the photovoltaic power supply mode is maintained; when the photovoltaic voltage change rate is greater than or equal to 0.5 V / s and less than or equal to 5 V / s, pre-discharge of the photovoltaics or the battery is performed through the storage inverter to realize voltage pre-synchronization, which is used for switching transition between the photovoltaic power supply mode and the battery power supply mode; when the photovoltaic voltage change rate is greater than 5 V / s, switching is performed from the photovoltaic power supply mode to the battery power supply mode.

6. The method for electricity consumption management based on composite energy supply and storage according to claim 1, characterized in that, The first threshold value is determined according to a battery temperature, a core device power consumption, weather and a power consumption period, and the determination is specifically: the first threshold value is preset to be 20%, correction coefficients are set according to the battery temperature, the core device power consumption, the weather and the power consumption period to adjust the preset first threshold value to determine a final first threshold value, wherein the correction coefficient corresponding to the battery temperature is positively correlated with the battery temperature, and the correction coefficient corresponding to the core device power consumption is positively correlated with the core device power consumption.

7. The method according to claim 6, wherein, The weather is specifically: weather is judged through photovoltaic voltage, when a photovoltaic voltage peak value is greater than 300 V, the weather is judged to be sunny, and the correction coefficient is set to be 0.8; when the photovoltaic voltage peak value is less than 200 V, the weather is judged to be overcast or rainy, and the correction coefficient is set to be 1.

3. When the photovoltaic voltage peak value is ≥ 200V and ≤ 300V, it is judged as cloudy, and the corresponding correction coefficient is set to 1.

8. The method for electricity consumption management based on composite energy supply and storage according to claim 6, characterized in that, The power consumption period is specifically: According to the power consumption statistics in the historical time interval, the valley power consumption period and the peak power consumption period are divided, In the valley power consumption period, the corresponding correction coefficient is set to 0.7, In the peak power consumption period, the corresponding correction coefficient is set to 1.

4.

9. A storage medium, characterized in that, The storage medium stores a computer program, and the computer program is executed to realize the steps of the power consumption management method based on composite energy supply and storage according to any one of claims 1 to 8.

10. A processing device, characterized by Including: A memory for storing a computer program; A processor for executing the computer program to realize the steps of the power consumption management method based on composite energy supply and storage according to any one of claims 1 to 8.

Images