A home energy storage energy scheduling method based on multi-source electric parameter fusion

Abstract

The present application relates to the technical field of energy management and intelligent scheduling of energy storage system, and particularly relates to a household energy storage energy scheduling method based on multi-source electric parameter fusion, comprising: obtaining electric parameters, power grid state, electricity price, historical power consumption and meteorological data through EMS; distributing electric energy according to the comparison of photovoltaic and load power, using PID closed loop to inhibit reverse power transmission when surplus and full charge, and controlling discharge to make up for the shortage when insufficient; combining meteorological and historical characteristics to predict the gap power during the low valley period and charging the grid as needed; calculating the power attenuation cost based on the battery SOH and SOC during the peak period, and controlling the discharge to cut the peak when the income condition is met; finally, arbitrating and limiting the multi-source instructions through a five-level safety priority state machine, and executing protection or switching to off-grid power supply when the power grid is abnormal. The present application realizes efficient local consumption of photovoltaic electric energy, fine economic scheduling considering battery life and high reliability fault protection.

Description

Technical Field

[0001] This invention relates to the field of energy management and intelligent scheduling technology for energy storage systems, and in particular to a home energy storage energy scheduling method based on the fusion of multi-source electrical parameters. Background Technology

[0002] With the advancement of dual-carbon goals and the popularization of distributed energy, home energy management systems integrating photovoltaic power generation and energy storage batteries have been widely used in the residential energy sector. In existing home energy storage system scheduling schemes, the energy management system typically relies on collecting data on photovoltaic power generation, load power consumption, and the state of charge (SOC) of the energy storage batteries. By setting fixed threshold logic, it achieves basic energy allocation modes such as "self-consumption of photovoltaic power with surplus power fed into the grid" or "charging during off-peak hours and discharging during peak hours." When photovoltaic power generation exceeds the household load power, the system prioritizes storing the excess energy in the energy storage batteries, and after the batteries are fully charged, it transmits the remaining power to the external power grid.

[0003] Existing technologies often lack real-time dynamic closed-loop control of the power fed back to the grid connection point after photovoltaic power generation is in excess and energy storage batteries are fully charged. This can easily cause grid voltage fluctuations or even grid back-feeding exceeding the limit, leading to grid impact and safety hazards. It also reduces the local self-consumption rate of household photovoltaic power. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a home energy storage energy dispatching method based on multi-source electrical parameter fusion, which aims to improve the grid voltage fluctuation and even grid backfeeding over the boundary, causing grid impact and safety hazards, while also reducing the local self-consumption rate of home photovoltaic power.

[0005] This invention provides the following technical solution: a home energy storage energy dispatching method based on multi-source electrical parameter fusion, comprising: S1. The Energy Management System (EMS) acquires real-time operating electrical parameters, grid status and time-of-use electricity price information, historical electricity consumption characteristics, and cloud-based forecast meteorological data. S2, EMS allocates electrical energy based on the comparison results of photovoltaic power and load power in the real-time operating electrical parameters: if the photovoltaic power is excessive, it prioritizes charging the energy storage battery system. After full charging, the inverter output power is adjusted according to the grid connection point power closed loop to suppress backfeeding. If the photovoltaic power is insufficient, when the real-time SOC in the real-time operating electrical parameters is higher than the preset discharge lower limit, the energy storage battery system is controlled to discharge to make up for the power gap. S3. During off-peak electricity price periods, the EMS combines the cloud-based predicted meteorological data with the historical electricity consumption characteristics to predict the power shortage during high-price periods, and controls the grid to supplement the energy storage battery system with the power shortage when the grid charging conditions are met. S4. During peak electricity price periods, the EMS calculates the battery's energy decay cost based on the real-time SOH and real-time SOC in the real-time operating electrical parameters. When the difference between the peak electricity price in the time-of-use electricity price information and the energy decay cost meets the revenue condition and the real-time SOC is higher than the preset discharge lower limit, the EMS controls the energy storage battery system to discharge peak shaving. S5 and EMS uniformly receive the control commands output by S2 to S4, arbitrate and limit the power according to the safety priority, and then issue them for execution. When the power grid status is abnormal, they perform protection or switch off-grid power supply.

[0006] Preferably, in S2, the step of prioritizing charging the energy storage battery system if there is excess photovoltaic power includes: EMS detects the enable status of the energy storage priority switch; When the energy storage priority switch is turned on, the photovoltaic power is controlled to charge the energy storage battery system first, and the remaining power is supplied to the household load; When the energy storage priority switch is turned off, the photovoltaic power is controlled to be supplied to the household load first, and the remaining power is used to charge the energy storage battery system.

[0007] Preferably, in S2, after full charging, the inverter output power is adjusted in a closed loop according to the grid connection point power to suppress backfeeding, including: EMS samples the grid-connected power according to a preset control cycle and calculates the deviation between the current reverse power and the preset anti-reverse current safety threshold. Based on the PID control algorithm, the power limit value is dynamically calculated according to the deviation and sent to the inverter to limit its maximum output power, so that the reverse power at the grid connection point is kept below the preset anti-reverse current safety threshold.

[0008] Preferably, in S2, controlling the energy storage battery system to discharge to make up for the power shortfall when the real-time SOC in the real-time operating electrical parameters is higher than a preset discharge lower limit includes: EMS calculates the power difference between real-time load power and real-time photovoltaic power according to a preset sampling period; The power difference is compared with the maximum allowable discharge power of the energy storage battery system, and the minimum value between the two is taken as the target discharge power. EMS dynamically sends discharge commands to the inverter based on the target discharge power, controlling the energy storage battery system to discharge and output power to fill the power difference.

[0009] Preferably, in S3, the EMS combines the cloud-based predicted meteorological data with the historical electricity consumption characteristics to predict the electricity shortage during the high-price period, including: EMS predicts the expected photovoltaic power generation during the next high electricity price period based on the irradiance and temperature in the cloud-based meteorological data, and predicts the expected load power consumption during the next high electricity price period based on the historical electricity consumption characteristics. The difference between the projected load power consumption and the projected photovoltaic power generation is taken as the power shortage during the high-price period.

[0010] Preferably, in S3, satisfying the grid charging conditions includes: EMS detected that the grid charging allow switch is in the open state, and the current system time is within the preset grid charging allow period.

[0011] Preferably, in S4, the EMS calculates the battery degradation cost based on the real-time SOH and real-time SOC in the real-time operating electrical parameters, including: EMS calculates the maximum remaining available capacity of the battery based on the real-time SOH. Based on the pre-stored DOD-cycle lifetime nonlinear mapping curve, the total number of cycles under deep discharge corresponding to the current real-time SOC is obtained; Divide the initial purchase cost of the battery by the product of the maximum remaining capacity and the total number of cycles to obtain the battery degradation cost at the current SOC state.

[0012] Preferably, in S4, the difference between the peak electricity price in the time-of-use pricing information and the cost per kilowatt-hour attenuation satisfies the revenue condition including: EMS obtains the actual average electricity purchase price during off-peak charging periods in real time. Calculate the overall cost per kilowatt-hour during peak discharge periods based on the combined charge and discharge efficiency of the inverter and energy storage battery system. When the peak electricity price is greater than the comprehensive cost per kilowatt-hour, and the difference between the two is greater than the preset target profit per unit of electricity, the revenue condition is determined to be met.

[0013] Preferably, in S5, after arbitration and power limiting according to safety priorities, the following steps are taken before execution: EMS constructs a control command priority state machine, dividing the control commands corresponding to S2 to S4 and grid protection from high to low as follows: protection commands when the grid state is abnormal, commands to limit grid power, peak-shaving discharge commands of S4, off-peak charging commands of S3, and basic energy allocation commands of S2. When multiple control commands are output simultaneously from S2 to S4, the EMS retains the highest priority control command according to the priority state machine and masks or modifies the lower priority control commands. The target output power after arbitration is compared with the inverter's rated power and the maximum allowable charging and discharging power of the energy storage battery system. The minimum value among the three is taken as the limiting target value and sent to the inverter for execution.

[0014] Preferably, in S5, performing protection or switching off-grid power supply when the grid condition is abnormal includes: When the abnormal grid condition is that the grid voltage or frequency exceeds the safe range, the EMS controls the inverter to perform power reduction operation or tiered disconnection of secondary loads. When the abnormal grid condition is a grid voltage failure and power outage, the EMS controls the grid connection switch to disconnect from the grid and controls the inverter to switch from power control mode to independent voltage source control mode to establish local AC bus voltage to maintain continuous power supply to important household loads.

[0015] The present invention has the following beneficial effects: 1. In this invention, by allocating electrical energy based on real-time electrical parameters through EMS and using an incremental PID algorithm to adjust the inverter output power in a closed loop after the energy storage is fully charged, the precise control of the power fed back to the grid connection point and the maximization of local consumption of photovoltaic power are achieved. This solves the problems of grid impact caused by excess photovoltaic power fed back to the grid and low photovoltaic self-consumption rate.

[0016] 2. In this invention, the energy shortage is predicted by combining meteorological data to carry out off-peak charging, and the battery degradation is quantified into the cost per kilowatt-hour based on the DOD-cycle life nonlinear mapping model to determine peak-valley arbitrage. This achieves refined energy scheduling that takes into account both battery degradation and peak-valley arbitrage, and solves the problem of traditional energy storage that blindly charges and discharges without considering battery loss, resulting in accelerated battery degradation and poor returns throughout the entire life cycle.

[0017] 3. In this invention, a five-level safety priority state machine is constructed for command arbitration and power limiting. When the grid loses voltage, the inverter is quickly switched to an independent voltage source control mode. This realizes the concurrent and safe issuance of control commands and the rapid voltage establishment in the off-grid state, solving the problem of equipment overload risk caused by multiple command conflicts and the problem of important loads not being able to be continuously powered when the grid fails. Attached Figure Description

[0018] Figure 1 This is a flowchart of a home energy storage energy dispatching method based on multi-source electrical parameter fusion proposed in this invention; Figure 2 This is a flowchart illustrating the basic energy allocation and anti-backflow closed-loop control of a home energy storage energy dispatching method based on multi-source electrical parameter fusion proposed in this invention. Figure 3 This is a flowchart of the high-price period power shortage prediction and off-peak electricity supplementation charging process for a home energy storage energy dispatching method based on multi-source electrical parameter fusion proposed in this invention. Figure 4 This is a flowchart of a peak shaving control method based on the cost and benefit conditions of energy decay per kilowatt-hour, proposed in this invention for a home energy storage energy dispatch method based on multi-source electrical parameter fusion. Figure 5This is a flowchart illustrating the control command priority arbitration, power limiting, and fault protection control process of a home energy storage energy dispatching method based on multi-source electrical parameter fusion proposed in this invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In embodiments of the present invention, the present invention provides a home energy storage energy dispatching method based on multi-source electrical parameter fusion, such as... Figures 1-5 As shown, it includes the following steps: S1. The Energy Management System (EMS) acquires real-time operating electrical parameters, grid status and time-of-use electricity price information, historical electricity consumption characteristics, and cloud-based forecast meteorological data. Specifically, when implementing the home energy storage energy dispatch method, the energy management system first executes step S1, which involves acquiring real-time operating electrical parameters, grid status and time-of-use electricity price information, historical electricity consumption characteristics, and cloud-based predicted meteorological data in parallel through various data communication interfaces and measurement channels, and establishing multi-source data fusion input.

[0021] For acquiring real-time operating electrical parameters, the energy management system uses a built-in electrical parameter sampling interrupt service routine to read data from various sensors and sampling circuits at fixed control cycles. Specifically, the system reads the total household load power, branch load current, and AC bus voltage collected by the smart meter installed in the main circuit of the household distribution box at a 100ms sampling cycle; simultaneously, it reads the photovoltaic power generation, photovoltaic output voltage, and current output by the DC and AC sampling modules of the photovoltaic inverter at a 100ms sampling cycle; the system periodically receives cell data packaged and sent by the energy storage battery management system via CAN bus or RS485 bus, extracting the real-time state of charge (SOC), state of health (SOH), battery terminal voltage, charging and discharging current, and real-time cell temperature of the energy storage battery system; the system also reads the grid-connected power, grid-connected voltage phase, frequency, and AC current sampled by the bidirectional meter installed at the grid-connected point at a 100ms sampling cycle, thereby completing the real-time perception of the system's operating status.

[0022] For acquiring grid status and time-of-use pricing information, the energy management system employs a data stream processing approach combining local hardware detection and remote interface calls. The system monitors the voltage amplitude and frequency of the AC grid in real time using an optocoupler-isolated sampling circuit. When the detected grid voltage amplitude is within 90% to 110% of the rated voltage of 220V and the grid frequency is within 49.5Hz to 50.5Hz, the system sets the local grid status flag to logic 1, indicating a normal grid connection. When the detected voltage or frequency exceeds these ranges, the system sets the local grid status flag to logic 0, indicating an abnormal grid status. Simultaneously, the system sends data requests to the local power company's service API interface via Ethernet or wireless communication to download the time-of-use pricing curve for the current date; or it directly reads the pre-stored time-of-use pricing mapping table in the local non-volatile memory to extract the specific values ​​of peak, mid-peak, flat, and off-peak prices for the entire 24-hour period, along with their corresponding start and end time windows.

[0023] To acquire historical electricity consumption characteristics, the energy management system accesses the locally stored historical database and extracts the historical load power time series with a sampling period of 1 minute over the past 30 days. The system first applies a moving average filter with a window length of 5 to this time series to remove high-frequency spike noise caused by sudden load changes. Then, the data is sliced ​​by natural day, and the sliced ​​data is categorized and aggregated according to weekdays and weekends. The mean and standard deviation of the load power at each minute node within 24 hours are calculated, constructing a 24×60 historical electricity consumption feature matrix, which is stored in the system memory for subsequent prediction steps.

[0024] To acquire cloud-based forecast meteorological data, the energy management system sends an HTTP request containing local geographic latitude and longitude coordinates to the meteorological data server via a network communication module. After receiving the data packet from the meteorological data server, the system parses it and extracts the meteorological forecast data for the next 24 hours with a time resolution of 1 hour, including the total solar radiation intensity, ambient temperature, humidity, and cloud cover percentage for each hour, and writes this data into the meteorological data buffer.

[0025] After completing the multi-source data acquisition and analysis, the energy management system performs near real-time prediction calculations of photovoltaic power generation and load power based on the acquired data. During the photovoltaic power generation prediction process, the system uses a photoelectric conversion physical model of the photovoltaic modules to calculate the expected photovoltaic power generation at the next prediction time. The calculation relationship is as follows: ; in, This represents the calculated projected photovoltaic power generation. The rated installed power of the photovoltaic array under standard test conditions is represented by 10kW in this embodiment; G represents the total solar radiation intensity at the corresponding forecast time extracted from the meteorological data buffer, in units of... ; This represents the reference constant for radiation intensity under standard test conditions, with a value of [value missing]. ; The power temperature coefficient of a photovoltaic module is represented by a value of [value missing]. ; This indicates the predicted operating temperature of the photovoltaic panel, calculated based on the predicted ambient temperature and light intensity. This represents the temperature reference constant under standard test conditions, with a value of [value missing]. .

[0026] In the process of predicting household load power, the energy management system combines historical electricity consumption characteristic matrices with predicted ambient temperature and uses a temperature correction model to calculate the expected load power consumption at the next prediction time. The calculation relationship is as follows: ; in, This represents the calculated estimated load power consumption; This represents the historical average load power corresponding to the current prediction time t, retrieved from the historical electricity consumption feature matrix; This represents the load's sensitivity to temperature changes, and its value is... ; This represents the ambient temperature at the corresponding forecast time, extracted from the meteorological data buffer. This represents the comfort reference temperature constant, with a value of [value missing]. The system calculates the expected photovoltaic power generation and expected load power consumption during the subsequent high electricity price period by integrating the expected photovoltaic power generation and expected load power consumption over time, thus completing the data preparation and pre-calculation in step S1.

[0027] S2 and EMS allocate power based on the comparison between photovoltaic power and load power in the real-time operating electrical parameters: if the photovoltaic power is excessive, it will prioritize charging the energy storage battery system. After full charging, the inverter output power will be adjusted according to the grid connection point power closed loop to suppress backfeeding. If the photovoltaic power is insufficient, the energy storage battery system will be controlled to discharge to make up for the power gap when the real-time SOC in the real-time operating electrical parameters is higher than the preset discharge lower limit. Furthermore, in S2, if there is excess photovoltaic power, priority is given to charging the energy storage battery system, including: EMS detects the enable status of the energy storage priority switch; When the energy storage priority switch is turned on, the photovoltaic power is controlled to charge the energy storage battery system first, and the remaining power is supplied to the household load. When the energy storage priority switch is turned off, the photovoltaic power is controlled to be supplied to the household load first, and the remaining power is used to charge the energy storage battery system.

[0028] Furthermore, in S2, after full charging, the inverter output power is adjusted in a closed loop according to the grid connection point power to suppress backfeeding, including: EMS samples the grid-connected power according to a preset control cycle and calculates the deviation between the current reverse power and the preset anti-backflow safety threshold. Based on the PID control algorithm, the power limit value is dynamically calculated according to the deviation and sent to the inverter to limit its maximum output power, so that the reverse power at the grid connection point is kept below the preset anti-reverse current safety threshold.

[0029] Furthermore, in S2, controlling the energy storage battery system to discharge to make up for the power shortfall when the real-time SOC in the real-time operating electrical parameters is higher than the preset discharge lower limit includes: EMS calculates the power difference between real-time load power and real-time photovoltaic power according to a preset sampling period; The power difference is compared with the maximum allowable discharge power of the energy storage battery system, and the minimum value between the two is taken as the target discharge power. EMS dynamically sends discharge commands to the inverter based on the target discharge power, controlling the discharge output of the energy storage battery system to make up for the power difference.

[0030] Specifically, after acquiring and integrating multi-source electrical parameters and external data in step S1, the Energy Management System (EMS) enters step S2. Based on the comparison results of the real-time collected photovoltaic power generation and the total household load power, it executes a dynamic power allocation strategy and triggers corresponding anti-reverse current closed-loop control or gap discharge compensation logic under different power surplus and deficit states.

[0031] During the specific execution of step S2, the energy management system compares the real-time photovoltaic power with the real-time load power at a control cycle of 100ms.

[0032] When the real-time photovoltaic power is greater than the real-time load power, the system is determined to be in a state of photovoltaic power surplus. The energy management system first reads the enable status flag of the energy storage priority switch in the local register.

[0033] When the enable flag of the energy storage priority switch is detected to be logic 1, indicating that the energy storage priority switch is on, the energy management system controls the photovoltaic inverter to prioritize charging the energy storage battery system with the electricity generated by the photovoltaic system through the bidirectional DC-DC converter. The system calculates the difference between the photovoltaic power generation and the maximum allowable charging power of the energy storage battery system. If there is still surplus power after the photovoltaic power generation meets the maximum allowable charging power of the energy storage battery system, the energy management system allocates the remaining AC power to the household load.

[0034] When the enable flag of the energy storage priority switch is detected to be logic 0, indicating that the energy storage priority switch is closed, the energy management system adjusts the power flow control logic to prioritize the supply of AC power output from the photovoltaic inverter to household loads. The system calculates the difference between the real-time photovoltaic power and the real-time load power, and directs the remaining power corresponding to this difference to the bidirectional DC-DC converter to charge the energy storage battery system.

[0035] During the process of excess photovoltaic power charging the energy storage battery system, the energy management system continuously monitors the real-time state of charge (SOC) and the maximum voltage of individual battery cells. When the real-time SOC reaches 100% or the voltage of any cell reaches the preset charging cutoff voltage threshold of 3.65V, the energy storage battery system is determined to be fully charged. The energy management system then stops charging the energy storage battery system and triggers the anti-reverse current closed-loop regulation logic to suppress the backflow of power to the external power grid.

[0036] During the anti-backflow closed-loop regulation process, the energy management system samples the bidirectional metering meters installed at the grid connection point at a preset control cycle of 100ms to obtain the real-time grid connection point power. The energy management system calculates the difference between the sampled grid connection point power value and the preset anti-backflow safety threshold to obtain the current reverse power deviation. The calculation relationship is as follows: ; in, Indicates the first The reverse power deviation per control cycle; This represents the real-time grid-connected power sampled during the kth control cycle, and the power value is defined as positive when power is supplied to the grid. This represents the preset backflow prevention safety threshold, which is set to 0.1kW in this embodiment. This threshold is used to retain a small margin for absorbing grid power and to prevent accidental power transmission to the grid due to measurement errors.

[0037] The energy management system will calculate the reverse power deviation. The input is fed into the built-in incremental PID controller to dynamically calculate the maximum allowable output power limit of the inverter for the next control cycle. The calculation relationship is as follows: ; ; in, Indicates the first Power limiting adjustment amount per control cycle; and They represent the first and the The reverse power deviation per control cycle; This represents the proportionality coefficient, with a value of 0.8. This represents the integral coefficient, with a value of 0.05. This represents the differential coefficient, with a value of 0.01. This represents the maximum allowable output power limit value of the inverter calculated in the k-th control cycle; This represents the maximum allowable output power limit of the inverter in the previous control cycle. The energy management system will calculate the... The control chip of the photovoltaic inverter is sent to the CAN bus in real time to limit the AC output power of the inverter, thereby continuously controlling the reverse power of the grid connection point within the preset anti-reverse current safety threshold. the following.

[0038] When the real-time photovoltaic power is less than the real-time load power, the system is determined to be in a state of insufficient photovoltaic power. The energy management system first compares the real-time SOC reported by the battery management system with the preset discharge lower limit threshold, which is set to 15%.

[0039] If the real-time SOC is lower than or equal to 15%, the energy management system will prohibit the energy storage battery system from discharging, and the insufficient power gap will be directly supplemented by the external power grid.

[0040] If the real-time SOC is higher than 15%, the energy management system initiates the discharge gap-filling control process. The system calculates the power difference between the real-time load power and the real-time photovoltaic power at a preset sampling period of 100ms. The calculation relationship is as follows: ; in, This represents the real-time power gap calculated in the k-th sampling period; This represents the total household load power obtained through real-time sampling; This represents the photovoltaic power generation obtained from real-time sampling.

[0041] The energy management system will calculate the power deficit. Compared with the current maximum allowable discharge power of the energy storage battery system The numerical values ​​are compared, and the minimum value between the two is taken as the target discharge power. The calculation relationship is as follows: ; in, The battery management system dynamically calculates and reports the current battery temperature, real-time SOC, and SOH health status in kW.

[0042] The energy management system will calculate the determined target discharge power. The commands are encapsulated as discharge control commands and sent to the bidirectional inverter via the communication interface, controlling the bidirectional inverter to drive the energy storage battery system to discharge at the target power. It discharges power to precisely fill the power difference between photovoltaic power and load power, enabling self-generation and self-consumption of household loads and power balancing.

[0043] S3. During off-peak electricity price periods, EMS combines cloud-based meteorological data and historical electricity consumption characteristics to predict the power shortage during high-price periods, and controls the grid to supplement the energy storage battery system with the power shortage when the grid charging conditions are met. Furthermore, in S3, EMS combines cloud-based forecast meteorological data with historical electricity consumption characteristics to predict the electricity shortage during periods of high prices, including: EMS predicts the expected photovoltaic power generation during the next high electricity price period based on irradiance and temperature data from cloud-based meteorological forecasts, and predicts the expected load power consumption during the next high electricity price period based on historical electricity consumption characteristics. The difference between the expected load power consumption and the expected photovoltaic power generation will be used as the power shortage during the high-price period.

[0044] Furthermore, in S3, meeting the grid charging conditions includes: EMS detected that the grid charging allow switch is in the open state, and the current system time is within the preset grid charging allow period.

[0045] Specifically, during off-peak electricity price periods, the energy management system enters step S3, executing high-price period deficit power prediction and grid off-peak electricity supplementation charging control. The energy management system compares the system clock with the preset time-of-use electricity price table in real time. When the current system time enters an off-peak electricity price period, such as from 00:00 to 06:00, the calculation and control process in step S3 is triggered. The system first retrieves the time-of-use electricity price table, identifies the upcoming peak electricity price period for the day, and defines the peak electricity price start time as... The peak electricity price ends at [time]. For example, the peak electricity price window is from 8:00 AM to 11:00 AM and from 5:00 PM to 9:00 PM.

[0046] To accurately predict the power shortage within the peak electricity price window, the energy management system calculates the projected photovoltaic (PV) power generation and projected load power consumption within that window. The energy management system extracts the total solar radiation intensity and ambient temperature for each hour within the peak electricity price window from the meteorological data buffer and substitutes them into the PV power generation prediction model in step S1 to calculate the projected PV power for each hour within the peak electricity price window. Then, according to the time step The estimated photovoltaic power generation for the next peak electricity price period is calculated by summing the results over a 1-hour period. The calculation relationship is as follows: ; in, This represents the calculated estimated photovoltaic power generation during peak electricity price periods; This represents the predicted photovoltaic power at time t; Indicates the time step, with a value of 1 hour; and These represent the start and end times of the peak electricity price period, respectively.

[0047] Simultaneously, the energy management system retrieves the historical average load power for each hour within the peak electricity price time window from the historical electricity consumption characteristic matrix, combines it with the predicted ambient temperature from meteorological data, and substitutes it into the load power prediction model in step S1 to calculate the predicted load power for each hour within the peak electricity price time window. Then, following the same time step... By summing the results, the estimated load power consumption for the next peak electricity price period can be calculated. The calculation relationship is as follows: ; in, This represents the estimated power consumption during peak electricity price periods, calculated accordingly. Indicates time The predicted power of the load.

[0048] After completing the above forecasts, the energy management system will estimate the load power consumption. With the expected power generation of photovoltaic By performing a difference calculation, the power shortage during the high-price period can be obtained, and the calculation relationship is as follows: ; in, This represents the electricity shortage during periods of high prices. If the calculated... A value less than or equal to 0 indicates that the photovoltaic power generation during peak electricity price periods is sufficient to cover load consumption, and the energy management system will allocate the shortfall power during high-price periods. Reset to 0, and do not trigger grid-based supplemental charging; if the calculated value is... If the value is greater than 0, then this value will be used as the target energy benchmark for subsequent off-peak charging.

[0049] The electricity shortage during periods of high prices (greater than zero) Afterwards, the energy management system determines whether the grid charging conditions are met. The energy management system reads the status flag of the energy storage grid charging allow switch from the local system configuration register, and simultaneously reads the current real-time system time. Only when the status flag of the grid charging allow switch is detected to be logic 1, and the current system time is within the preset grid charging allowable time period (e.g., 01:00 AM to 05:00 AM), the energy management system determines that the grid charging conditions are met and allows the grid supplementary charging process to begin. If the status flag of the grid charging allow switch is logic 0, or the current system time is not within the preset grid charging allowable time period, the energy management system determines that the grid charging conditions are not met and maintains the grid charging circuit disconnected.

[0050] After determining that the grid charging conditions are met, the energy management system calculates the final target supplementary charging amount and target charging power. The system first reads the real-time state of charge of the energy storage battery system reported by the battery management system. and the current maximum available capacity Calculate the state of charge of the energy storage battery system from its current state to the target off-peak charging point. The maximum amount of electricity that can be received, of which The value is set at 95%, and the calculation relationship is as follows: ; in, This indicates the maximum rechargeable capacity of the energy storage battery system; This represents the current maximum available capacity of the energy storage battery system, which is taken as 15kWh in this embodiment; This indicates the state of charge at the end of the off-peak electricity target charging phase, with a value of 95. This indicates the current real-time state of charge, with a value ranging from 0 to 100.

[0051] The energy management system will manage the shortfall in electricity during periods of high prices. With maximum rechargeable capacity The two values ​​are compared, and the minimum value is taken as the final target to replenish the charging capacity. The calculation relationship is as follows: ; Subsequently, the energy management system determines the remaining duration of the preset grid-allowed charging time period. The target charging power is calculated using the following relationship: ; in, Indicates the target grid charging power; This represents the combined charging efficiency of the energy storage battery system and the bidirectional AC-DC converter, with a value of 0.94. This indicates the number of hours remaining until the end of the permitted charging period on the power grid.

[0052] The energy management system will calculate the target grid charging power. The maximum allowable grid charging power limit (e.g., 3.3kW) is compared with the maximum charging power allowed by the battery management system, and the minimum of these three values ​​is taken as the final command power and sent to the bidirectional AC-DC converter. The bidirectional AC-DC converter starts operation and controls the grid to charge the energy storage battery system according to the command power. The energy management system integrates the charging energy input from the grid in real time during the charging process. When the accumulated charging energy reaches... Or the real-time state of charge reaches If the system time exceeds the preset grid-allowed charging time period, the energy management system will issue a stop charging command, shut down the grid charging circuit, and complete the pre-replenishment of the power shortage during the high-price period.

[0053] S4. During peak electricity price periods, the EMS calculates the battery's energy decay cost based on the real-time SOH and real-time SOC in the real-time operating electrical parameters. When the difference between the peak electricity price and the energy decay cost in the time-of-use electricity price information meets the revenue condition and the real-time SOC is higher than the preset discharge lower limit, the EMS controls the energy storage battery system to discharge peaks. Furthermore, in S4, the EMS calculates the battery's energy degradation cost based on the real-time SOH and real-time SOC parameters in the real-time operating electrical parameters, including: EMS calculates the maximum remaining available capacity of the battery based on the real-time SOH. Based on the pre-stored DOD-cycle lifetime nonlinear mapping curve, the total number of cycles under deep discharge corresponding to the current real-time SOC is obtained; Divide the initial purchase cost of the battery by the product of the maximum remaining capacity and the total number of cycles to obtain the battery's per-unit charge degradation cost at the current state of charge (SOC).

[0054] Furthermore, in S4, the difference between the peak electricity price and the cost per kilowatt-hour degradation in the time-of-use electricity pricing information satisfies the revenue conditions including: EMS obtains the actual average electricity purchase price during off-peak charging periods in real time. Calculate the overall cost per kilowatt-hour during peak discharge periods based on the combined charge and discharge efficiency of the inverter and energy storage battery system. When the peak electricity price is greater than the comprehensive cost per kilowatt-hour, and the difference between the two is greater than the preset target profit per unit of electricity, the revenue condition is deemed to be met.

[0055] Specifically, when the system's real-time clock enters the preset peak electricity price period, such as from 8:00 AM to 11:00 AM or from 5:00 PM to 9:00 PM, the Energy Management System (EMS) triggers step S4 to initiate a discharge peak shaving control process based on the battery's energy decay cost and real-time electricity price revenue assessment.

[0056] During the execution of step S4, the energy management system first calls the electrical parameter data channel to obtain the real-time health status (SOH) and real-time state of charge (SOC) of the energy storage battery system from the battery management system. Then, it combines the battery initial purchase cost and DOD-cycle lifetime nonlinear mapping model pre-stored in the local nonvolatile memory to accurately calculate the battery power degradation cost under the current SOC state.

[0057] Specifically, the energy management system first calculates the maximum remaining available capacity of the energy storage battery system based on the real-time State of Health (SOH), and the calculation relationship is as follows: ; in, This represents the calculated maximum remaining capacity of the energy storage battery system. This indicates the factory rated capacity of the energy storage battery system, which is taken as 15kWh in this embodiment; This represents the current real-time health status percentage, ranging from 0 to 100.

[0058] Subsequently, the energy management system converts the real-time State of Charge (SOC) into the corresponding Depth of Discharge (DOD), which is the difference between the DOD value (equal to 100) and the real-time SOC. The system inputs this DOD value into a pre-stored DOD-cycle life nonlinear mapping model to calculate the total number of cycles the battery can withstand at the current DOD. In this embodiment, the DOD-cycle life nonlinear mapping model is constructed using a power-law function model, and its calculation relationship is as follows: ; in, This represents the total number of battery cycles calculated at the depth of discharge corresponding to the current real-time SOC. This represents the current real-time state of charge percentage, ranging from 0 to 100. This represents the pre-fitted model coefficients, with a value of 150000; This represents the power-law exponent of the prefitted model, with a value of 1.2.

[0059] After obtaining the maximum remaining capacity Total number of loops Then, the energy management system calculates the battery's energy degradation cost at the current state of charge (SOC), and the calculation relationship is as follows: ; in, This represents the calculated cost per kilowatt-hour of battery energy degradation, expressed in yuan / kWh. This represents the initial purchase cost of the energy storage battery system, which is set to 18,000 yuan in this embodiment.

[0060] After calculating the cost per kilowatt-hour of battery energy degradation, the energy management system performs a real-time assessment of the overall cost per kilowatt-hour and revenue conditions during peak discharge periods. The energy management system first retrieves the actual electricity purchase data recorded and stored during the off-peak charging period in step S3, and divides the total electricity purchase amount during the off-peak charging period by the total charging amount to calculate the actual average electricity purchase price during the off-peak charging period. Subsequently, the system combines the combined charge and discharge efficiency of the energy storage battery system and the bidirectional inverter to calculate the overall cost per kilowatt-hour during peak discharge periods. The calculation relationship is as follows: ; in, This represents the calculated comprehensive cost per kilowatt-hour during peak discharge periods, expressed in yuan / kWh. This represents the actual average electricity purchase price during off-peak charging periods, which is taken as 0.35 yuan / kWh in this embodiment; This represents the combined charging efficiency of the energy storage battery system and the inverter, with a value of 0.94. This represents the combined discharge efficiency of the energy storage battery system and the inverter, with a value of 0.94. This represents the battery energy degradation cost calculated above, expressed in yuan / kWh.

[0061] The energy management system reads the real-time peak electricity price for the current peak electricity price period from the time-of-use electricity price table and compares it with the comprehensive cost per kilowatt-hour. Comparative analysis is needed. Only when the real-time peak electricity price exceeds the comprehensive cost per kilowatt-hour... When the difference between the two is greater than or equal to the preset target profit threshold per unit of electricity, the energy management system determines that the revenue condition is met. The judgment logic is as follows: ; in, This represents the current peak electricity price retrieved from the time-of-use electricity price information, which is set to 1.25 yuan / kWh in this embodiment; This represents the comprehensive cost per kilowatt-hour during peak discharge periods, expressed in yuan / kWh. This represents the preset target profit per unit of electricity, which is set to 0.15 yuan / kWh in this embodiment.

[0062] Upon determining that the benefit conditions are met, the energy management system compares the real-time SOC reported by the battery management system with the preset discharge lower limit threshold. If the real-time SOC is 15% higher than the preset discharge lower limit threshold and the above benefit condition judgment formula is met, the energy management system officially triggers the discharge peak shaving control logic.

[0063] During the discharge peak-shaving control process, the energy management system dynamically calculates the target discharge power based on the real-time collected total household load power and the expected photovoltaic power generation, with a control cycle of 100ms. It then sends discharge control commands to the bidirectional inverter via a communication interface. The bidirectional inverter drives the energy storage battery system to discharge externally, prioritizing supply to local household loads and smoothing out load peaks when photovoltaic power is insufficient. This reduces the amount of electricity purchased from the grid at high prices, maximizing economic benefits during periods of high electricity prices and precisely controlling battery lifespan degradation.

[0064] S5 and EMS uniformly receive control commands output from S2 to S4, arbitrate and limit power according to safety priority, and then issue them for execution. They also perform protection or switch off-grid power supply when the grid status is abnormal.

[0065] Furthermore, in S5, the execution process after arbitration and power limiting according to safety priorities includes: EMS constructs a control command priority state machine, dividing the control commands corresponding to S2 to S4 and grid protection from high to low as follows: protection commands when the grid state is abnormal, commands to limit grid power, peak-shaving discharge commands of S4, off-peak charging commands of S3, and basic energy allocation commands of S2. When multiple control commands are output simultaneously from S2 to S4, the EMS retains the highest priority control command according to the priority state machine and masks or modifies the lower priority control commands. The target output power after arbitration is compared with the inverter's rated power and the maximum allowable charging and discharging power of the energy storage battery system. The minimum value among the three is taken as the limiting target value and sent to the inverter for execution.

[0066] Furthermore, in S5, performing protection or switching off-grid power supply when the grid condition is abnormal includes: When the grid condition is abnormal, such as when the grid voltage or frequency exceeds the safe range, the EMS controls the inverter to perform power reduction operation or tiered disconnection of secondary loads. When the power grid is abnormal and experiences a power outage, the EMS controls the grid-connected switch to disconnect from the grid and switches the inverter from power control mode to independent voltage source control mode to establish local AC bus voltage to maintain continuous power supply to critical household loads. Specifically, after completing the power allocation and algorithm calculations in steps S2 to S4, the energy management system proceeds to step S5, where it uniformly receives various control commands output in real time from steps S2, S3, and S4 through the kernel instruction arbitration engine. The energy management system reads the instruction output registers of each module at a 50ms arbitration control cycle and, in conjunction with the real-time output of the grid status safety detection module, performs arbitration judgment based on safety priority and applies three levels of power limiting to all commands. Finally, it sends the determined target control parameters to the bidirectional inverter and actuators, and immediately triggers safety protection or off-grid power supply switching procedures when an abnormal grid status is detected.

[0067] During the command arbitration process, the energy management system first constructs a control command priority state machine. This state machine divides the received control commands into five safety priority levels, from highest to lowest: priority level 1 is the protection command when the grid state is abnormal; priority level 2 is the command to limit grid power; priority level 3 is the peak-shaving discharge command in step S4; priority level 4 is the off-peak supplementary charging command in step S3; and priority level 5 is the basic energy allocation command in step S2. When multiple control commands are output simultaneously in steps S2 to S4 within the same control cycle, the energy management system invokes the arbitration logic of the priority state machine and automatically retains the currently triggered highest priority control command as the dominant command. For control commands with lower priorities, the energy management system either directly masks them or modifies their values ​​according to the highest priority constraint conditions. For example, when the basic energy allocation command in step S2 requests power to be fed back to the grid, and the grid power limiting command in step S5 is triggered, the energy management system masks the original command in step S2 and corrects the target power to a value that does not exceed the preset anti-reverse current safety threshold.

[0068] After determining the target output power after arbitration, the energy management system performs a three-level safety power limiting calculation. The system compares the initial target output power obtained from arbitration with the rated AC power of the bidirectional inverter and the maximum allowable charge / discharge power reported in real time by the energy storage battery management system. The minimum of these three values ​​is taken as the final limiting target power issued to the inverter. When the arbitration output command is in the charging state, the final grid or photovoltaic charging power calculation formula is as follows: ; In the above formula, This indicates the target charging power that will ultimately be sent to the inverter for execution. This indicates the initial target charging power output after priority arbitration. This represents the rated AC power of the bidirectional inverter, which is taken as 10kW in this embodiment; This indicates the maximum allowable charging power reported in real time by the battery management system based on the current cell temperature and state of charge. In this embodiment, the value is 8kW.

[0069] When the arbitration output command direction is discharge state, the final discharge power calculation formula is as follows: ; In the above formula, This indicates the target discharge power that is ultimately sent to the inverter for execution; This indicates the initial target discharge power output after priority arbitration; This indicates the maximum allowable discharge power reported in real time by the battery management system based on the current cell temperature and state of charge; in this embodiment, the value is taken as 8kW. The energy management system sends the calculated value to the power regulation register of the inverter control chip via the communication bus for execution.

[0070] Throughout the system's operation, the grid status monitoring module in step S5 verifies the sampled grid voltage amplitude and frequency in real time. When a grid status anomaly is detected where the grid voltage or frequency exceeds the safe range—for example, a grid voltage amplitude below 198V or above 242V, or a grid frequency below 49.5Hz or above 50.5Hz—but a voltage signal still exists on the grid bus, the energy management system triggers a first-stage grid protection logic. The system first controls the inverter to perform power reduction operation, linearly reducing the inverter's output power by 10% per second. If the grid parameters still do not return to the safe range after power reduction operation, the energy management system sends a tiered disconnection control signal to the AC distribution box through the input / output control interface, prioritizing the disconnection of power supply circuits for secondary loads such as air conditioners and electric water heaters, reducing the impact of local loads on the grid, and maintaining the stability of the voltage at key system nodes.

[0071] When a grid status anomaly is detected as a grid outage, such as a voltage drop below 50V and then to 0V within 20ms, the energy management system determines that the external grid is out of power and immediately triggers the off-grid switching protection. Within 5ms, the energy management system sends a trip command to the grid-connected AC contactor, disconnecting the grid-connected switch to physically disconnect the home microgrid from the external grid and prevent power from being supplied to the out-of-voltage grid island. Simultaneously, the energy management system sends a mode switching command to the bidirectional inverter via the communication bus, switching the inverter's control loop from the grid-following power control mode to an independent voltage source control mode. The inverter re-establishes a standard sinusoidal AC bus voltage of 220V and 50Hz, powered by the energy storage battery system or photovoltaic array, enabling seamless off-grid continuous power supply to critical home loads.

[0072] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for home energy storage dispatching based on multi-source electrical parameter fusion, characterized in that, include: S1. The Energy Management System (EMS) acquires real-time operating electrical parameters, grid status and time-of-use electricity price information, historical electricity consumption characteristics, and cloud-based forecast meteorological data. S2, EMS allocates electrical energy based on the comparison results of photovoltaic power and load power in the real-time operating electrical parameters: if the photovoltaic power is excessive, it prioritizes charging the energy storage battery system. After full charging, the inverter output power is adjusted according to the grid connection point power closed loop to suppress backfeeding. If the photovoltaic power is insufficient, when the real-time SOC in the real-time operating electrical parameters is higher than the preset discharge lower limit, the energy storage battery system is controlled to discharge to make up for the power gap. S3. During off-peak electricity price periods, the EMS combines the cloud-based predicted meteorological data with the historical electricity consumption characteristics to predict the power shortage during high-price periods, and controls the grid to supplement the energy storage battery system with the power shortage when the grid charging conditions are met. S4. During peak electricity price periods, the EMS calculates the battery's energy decay cost based on the real-time SOH and real-time SOC in the real-time operating electrical parameters. When the difference between the peak electricity price in the time-of-use electricity price information and the energy decay cost meets the revenue condition and the real-time SOC is higher than the preset discharge lower limit, the EMS controls the energy storage battery system to discharge peak shaving. S5 and EMS uniformly receive the control commands output by S2 to S4, arbitrate and limit the power according to the safety priority, and then issue them for execution. When the power grid status is abnormal, they perform protection or switch off-grid power supply.

2. The home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S2, the step of prioritizing charging the energy storage battery system if there is excess photovoltaic power includes: EMS detects the enable status of the energy storage priority switch; When the energy storage priority switch is turned on, the photovoltaic power is controlled to charge the energy storage battery system first, and the remaining power is supplied to the household load; When the energy storage priority switch is turned off, the photovoltaic power is controlled to be supplied to the household load first, and the remaining power is used to charge the energy storage battery system.

3. The home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S2, after full charging, the inverter output power is adjusted in a closed loop according to the grid connection point power to suppress backfeeding, including: EMS samples the grid-connected power according to a preset control cycle and calculates the deviation between the current reverse power and the preset anti-reverse current safety threshold. Based on the PID control algorithm, the power limit value is dynamically calculated according to the deviation and sent to the inverter to limit its maximum output power, so that the reverse power at the grid connection point is kept below the preset anti-reverse current safety threshold.

4. The home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S2, controlling the energy storage battery system to discharge to make up for the power shortfall when the real-time SOC in the real-time operating electrical parameters is higher than the preset discharge lower limit includes: EMS calculates the power difference between real-time load power and real-time photovoltaic power according to a preset sampling period; The power difference is compared with the maximum allowable discharge power of the energy storage battery system, and the minimum value between the two is taken as the target discharge power. EMS dynamically sends discharge commands to the inverter based on the target discharge power, controlling the energy storage battery system to discharge and output power to fill the power difference.

5. A home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S3, the EMS combines the cloud-based predicted meteorological data with the historical electricity consumption characteristics to predict the electricity shortage during high-price periods, including: EMS predicts the expected photovoltaic power generation during the next high electricity price period based on the irradiance and temperature in the cloud-based meteorological data, and predicts the expected load power consumption during the next high electricity price period based on the historical electricity consumption characteristics. The difference between the projected load power consumption and the projected photovoltaic power generation is taken as the power shortage during the high-price period.

6. The home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S3, satisfying the grid charging conditions includes: EMS detected that the grid charging allow switch is in the open state, and the current system time is within the preset grid charging allow period.

7. A home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S4, the EMS calculates the battery degradation cost based on the real-time SOH and real-time SOC in the real-time operating electrical parameters, including: EMS calculates the maximum remaining available capacity of the battery based on the real-time SOH. Based on the pre-stored DOD-cycle lifetime nonlinear mapping curve, the total number of cycles under deep discharge corresponding to the current real-time SOC is obtained; Divide the initial purchase cost of the battery by the product of the maximum remaining capacity and the total number of cycles to obtain the battery degradation cost at the current SOC state.

8. A home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S4, the difference between the peak electricity price in the time-of-use electricity price information and the cost per kilowatt-hour of electricity attenuation satisfies the revenue condition including: EMS obtains the actual average electricity purchase price during off-peak charging periods in real time. Calculate the overall cost per kilowatt-hour during peak discharge periods based on the combined charge and discharge efficiency of the inverter and energy storage battery system. When the peak electricity price is greater than the comprehensive cost per kilowatt-hour, and the difference between the two is greater than the preset target profit per unit of electricity, the revenue condition is determined to be met.

9. A home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S5, the process of arbitration and power limiting based on safety priorities before execution includes: EMS constructs a control command priority state machine, dividing the control commands corresponding to S2 to S4 and grid protection from high to low as follows: protection commands when the grid state is abnormal, commands to limit grid power, peak-shaving discharge commands of S4, off-peak charging commands of S3, and basic energy allocation commands of S2. When multiple control commands are output simultaneously from S2 to S4, the EMS retains the highest priority control command according to the priority state machine and masks or modifies the lower priority control commands. The target output power after arbitration is compared with the inverter's rated power and the maximum allowable charging and discharging power of the energy storage battery system. The minimum value among the three is taken as the limiting target value and sent to the inverter for execution.

10. A home energy storage energy dispatching method based on multi-source electrical parameter fusion according to claim 1, characterized in that, In S5, performing protection or switching off-grid power supply when the power grid state is abnormal includes: When the abnormal grid condition is that the grid voltage or frequency exceeds the safe range, the EMS controls the inverter to perform power reduction operation or tiered disconnection of secondary loads. When the abnormal grid condition is a grid voltage failure and power outage, the EMS controls the grid connection switch to disconnect from the grid and controls the inverter to switch from power control mode to independent voltage source control mode to establish local AC bus voltage to maintain continuous power supply to important household loads.

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