Energy storage device and energy storage control system

Abstract

The invention relates to the technical field of power system energy storage, in particular to an energy storage device and an energy storage control system, which comprise a state monitoring module, a load demand interface, a control decision module and a power regulation module. The state monitoring module collects battery operation parameters in real time, the load demand interface receives power grid load signals, the control decision module generates charging and discharging instructions based on the information, and the power adjusting module executes power adjustment. The system can automatically select a control strategy according to a power grid demand type, achieves the functions of battery health state protection, power grid frequency modulation and peak regulation, renewable energy fluctuation smoothing and the like, effectively improves the response speed of an energy storage system, prolongs the service life of a battery, and improves the power grid stability and the energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage technology, and more specifically, to an energy storage device and an energy storage control system. Background Technology

[0002] Energy storage control systems belong to the field of power system energy storage technology. With the large-scale grid connection of renewable energy and the advancement of smart grid construction, battery energy storage systems are increasingly widely used in power peak shaving, frequency regulation, and backup power. In recent years, energy storage systems have been developing towards intelligence, efficiency, and systematization, placing higher demands on control accuracy, response speed, and battery life management.

[0003] Existing technologies mostly employ control strategies based on simple thresholds, which are difficult to adapt to complex and ever-changing grid demand environments. Current energy storage control systems suffer from problems such as slow response, insufficient consideration of battery health status, and a lack of multi-objective optimization capabilities. In particular, they cannot balance battery life and grid regulation performance when dealing with frequent grid fluctuations. This invention aims to address the shortcomings of existing energy storage control systems in areas such as battery life protection, grid demand response speed, and multi-objective coordinated control, thereby improving the overall performance of the system. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an energy storage device and an energy storage control system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an energy storage control system, applied to an energy storage device including a battery storage system, the control system comprising: The status monitoring module is used to collect the operating parameters of the battery storage system in real time, including the battery state of charge, charging and discharging current and battery temperature. The load demand interface is used to receive load demand signals sent by the external power grid. The control decision module, connected to the status monitoring module and the load demand interface, is configured to generate charging and discharging instructions based on the load demand signal and the operating parameters. The charging and discharging instructions include a power setting value and a time parameter for charging or discharging. A power regulation module, connected to the control decision module and the battery storage system, is configured to adjust the charging and discharging power of the battery storage system according to the charging and discharging command, so as to match the grid load demand.

[0006] Alternatively, the status monitoring module is also used to collect battery health status parameters, and the control decision module is further configured to: when generating the charge / discharge command, compare the battery health status parameters with a preset threshold, and limit the absolute value of the charge / discharge power according to the comparison result, so as to keep the battery health status parameters within a preset range.

[0007] If multiple options are selected, the control decision module is configured to perform the following operations: When the battery health status parameter indicates that the battery aging degree exceeds the first threshold, the absolute value of the maximum allowable charge and discharge power will be reduced from the rated value to the first preset value. When the battery health status parameter indicates that the battery internal resistance exceeds the second threshold, the adjustment rate of the charging and discharging power is limited to below the second preset value.

[0008] If multiple options are selected, the control decision module is configured as follows: Analyze the load demand signal to identify the type of power grid demand, such as peak shaving, frequency regulation, or emergency backup. Select the corresponding charging and discharging control strategy according to different demand types. The peak shaving strategy corresponds to continuous charging and discharging power control, the frequency modulation strategy corresponds to fast power response control at the second or minute level, and the emergency backup strategy corresponds to instantaneous power start-up control in standby mode.

[0009] Multiple options are available, and the control decision module is configured under the frequency modulation strategy as follows: Calculate the required power compensation based on the frequency deviation and frequency change rate in the load demand signal; Based on the required power compensation amount and the battery state of charge, a dynamically changing power setpoint is generated, and the power output of the battery storage system is adjusted through the power regulation module.

[0010] Alternatively, the control system may also include an operation log module, which records historical operating parameters collected by the status monitoring module and historical instruction data generated by the control decision module, and generates a battery performance degradation trend report based on the historical data.

[0011] Alternatively, the load demand interface is also configured to receive output forecast information from renewable energy power generation equipment, and the control decision module is further configured to generate pre-charge or pre-discharge instructions in advance based on the comparison results between the output forecast information and the real-time load demand signal, so as to smooth the output fluctuations of renewable energy.

[0012] Alternatively, the power regulation module includes a bidirectional converter and a control circuit connected thereto. The control circuit is configured to receive the power setpoint and control the conduction state of the bidirectional converter through pulse width modulation to achieve continuous regulation of the charging and discharging power of the battery storage system.

[0013] Alternatively, the control decision module generates a stop discharge command when the battery state of charge is below the minimum protection threshold, and generates a stop charging command when the battery state of charge is above the maximum protection threshold, and the priority of the command is higher than other load demand response commands.

[0014] Alternatively, the control system may also include an alarm module, which generates a visual or audible alarm signal when the operating parameters exceed the safe range, and simultaneously sends an instruction to the control decision module to trigger the system to enter a protective charging and discharging mode.

[0015] The technical effects and advantages of this invention are as follows: Compared to existing technologies, this invention collects battery health status parameters in real time through a status monitoring module and dynamically adjusts charging and discharging power limits based on preset thresholds, thereby achieving online assessment and protection of battery health status, effectively extending battery life and improving system reliability.

[0016] Compared with existing technologies, this invention identifies the type of power grid demand and automatically selects the corresponding control strategy through the control decision module, and generates charging and discharging commands using a multi-time-scale optimization algorithm, which significantly improves the response speed and control accuracy of power grid frequency regulation and peak shaving, and enhances power grid stability.

[0017] Compared to existing technologies, this invention accesses renewable energy forecast information through a load demand interface and generates pre-charge / pre-discharge commands in advance by combining model predictive control algorithms. This effectively smooths out fluctuations in renewable energy output, improves energy utilization efficiency, and enhances the system's coordination and control capabilities. Attached Figure Description

[0018] Figure 1 This is a diagram of the overall system framework of the present invention.

[0019] Figure 2 This is a flowchart of the control decision module of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 As attached Figures 1-2 The energy storage control system shown is applied to an energy storage device including a battery storage system. The control system includes: a status monitoring module, used to collect the operating parameters of the battery storage system in real time, including battery state of charge, charging and discharging current and battery temperature. The load demand interface is used to receive load demand signals sent by the external power grid. The control decision module, connected to the status monitoring module and the load demand interface, is configured to generate charging and discharging instructions based on the load demand signal and the operating parameters. The charging and discharging instructions include a power setting value and a time parameter for charging or discharging. A power regulation module, connected to the control decision module and the battery storage system, is configured to adjust the charging and discharging power of the battery storage system according to the charging and discharging command, so as to match the grid load demand.

[0022] The state monitoring module collects voltage, current, and temperature parameters of individual battery cells through a distributed sensor network, and uses a Kalman filter algorithm to process the collected data in real time to eliminate measurement noise and obtain accurate estimates of the battery's state of charge.

[0023] State of charge (SOC) estimation based on Kalman filtering: in, The states of charge (SOC) of the battery at time k and time k-1 are dimensionless.

[0024] Coulomb efficiency, dimensionless.

[0025] The sampling current (A) at time k, positive for charging and negative for discharging.

[0026] : Sampling time interval (s).

[0027] : Rated battery capacity (Ah).

[0028] They are respectively process noise and observation noise.

[0029] : The measured value of the battery terminal voltage (V) at time k.

[0030] The open-circuit voltage (V) of the battery under this state of charge is a function of SOC.

[0031] Battery internal resistance (Ω).

[0032] This set of formulas constitutes the state equation and observation equation of the Kalman filter algorithm. By combining the advantages of both current integration (ampere-hour method) and voltage observation (open-circuit voltage method) and considering noise interference, it calculates a more accurate and stable SOC estimate in real time than a single algorithm. The module has a built-in self-diagnostic function, automatically switching to redundant sensors to continue operation when a sensor malfunction is detected.

[0033] The load demand interface adopts the industrial Ethernet communication protocol and supports both IEC 61850 and Modbus TCP communication protocols. It can parse the load demand signal sent by the power grid dispatch center, which includes power command, timestamp and command priority identifier.

[0034] The control decision module adopts a multi-objective optimization algorithm, with the optimization objectives of maximizing battery life and maximizing grid demand satisfaction. It establishes a decision model based on model predictive control, which optimizes the charging and discharging strategy every 5 minutes.

[0035] The objective function for Model Predictive Control (MPC) is: Where J represents the objective function value that needs to be minimized.

[0036] : Predict the length of the time domain.

[0037] : The reference value (kW) of the power demand of the power grid at time k+j.

[0038] : The planned output power (kW) of the battery system at time k+j.

[0039] The effective value of the battery current (A) at time k+j is positively correlated with battery loss.

[0040] : Weighting coefficients, used to balance the accuracy of tracking grid demand and the importance of reducing battery wear.

[0041] The power regulation module uses a bidirectional converter composed of fully controlled power devices. It achieves precise power control through space vector pulse width modulation technology with a control cycle of 100 microseconds, enabling smooth switching of charging and discharging power and impact-free grid connection.

[0042] The status monitoring module is also used to collect battery health status parameters, and the control decision module is further configured to: when generating the charge and discharge command, compare the battery health status parameters with a preset threshold, and limit the absolute value of the charge and discharge power according to the comparison result, so as to keep the battery health status parameters within a preset range.

[0043] The state monitoring module measures the change in battery internal resistance using an electrochemical impedance spectroscopy analyzer, calculates the battery capacity decay rate using incremental capacity analysis, and establishes a battery health state prediction model based on deep neural networks by combining historical data on cycle number and operating temperature.

[0044] This module performs a complete health status assessment every 24 hours, evaluating parameters including capacity retention, internal resistance growth rate, and maximum available power degradation coefficient. The control decision module sets multi-level health status thresholds. When a health status indicator falls below the first threshold, a power derating strategy is activated, linearly reducing the maximum allowable charge / discharge power according to the degree of health status degradation, with the derating coefficient gradually decreasing from 1.0 to 0.6. When a health status indicator falls below the second threshold, in addition to power limiting, a charge / discharge rate limiting function is also activated, controlling the power change rate to below 50% of the rated value. All health status parameters and limiting strategies are recorded in the battery health profile, forming a complete lifecycle management record.

[0045] The control decision module is configured to perform the following operations: when the battery health status parameter indicates that the battery aging degree exceeds a first threshold, the absolute value of the maximum allowable charge and discharge power is reduced from the rated value to a first preset value; when the battery health status parameter indicates that the battery internal resistance exceeds a second threshold, the adjustment rate of the charge and discharge power is limited to below a second preset value.

[0046] The control decision module has a built-in state machine mechanism to monitor the changing trends of health status parameters in real time. When the capacity retention rate is detected to be below 80% or the internal resistance growth rate is detected to be above 30%, the first-level protection strategy is triggered, which limits the maximum charging and discharging power to 80% of the rated value by modifying the power limit parameter.

[0047] Simultaneously, a power smoothing algorithm is activated, using a first-order inertial element to filter the power command, increasing the power change time constant from the default 200 milliseconds to 500 milliseconds. When the internal resistance value is detected to exceed 40% of the initial value, the second-level protection strategy is triggered, adding a power change rate limit to the power limit, reducing the maximum power change rate from 100% / second of the rated value to 50% / second.

[0048] These protection strategies employ a layered implementation: first, power limiting is applied, then the rate of change is limited, and finally, a ramp function is used to generate the final execution command. All protection parameters can be adjusted through a configuration interface and include a security authentication mechanism to prevent misoperation.

[0049] The control decision module is configured to: parse the load demand signal and identify the peak shaving, frequency regulation, or emergency backup demand type of the power grid; select the corresponding charging and discharging control strategy according to the different demand types, wherein the peak shaving strategy corresponds to continuous charging and discharging power control, the frequency regulation strategy corresponds to fast power response control at the second or minute level, and the emergency backup strategy corresponds to instantaneous power start-up control in standby mode.

[0050] The control decision module is equipped with a multi-strategy selector, capable of automatically identifying the command type identifier in the load demand signal. For peak-shaving demand, a power planning algorithm is employed to formulate a charging and discharging plan with a 15-minute time resolution, considering time-of-use pricing and load forecast data, and using mixed-integer programming to solve for the optimal power curve. For frequency regulation demand, a fast-response controller is deployed with a sampling period of 100 milliseconds. A proportional control algorithm with dead time tracks the system frequency deviation in real time, and power compensation is initiated when the frequency deviation exceeds 0.05Hz. The compensation amount has a piecewise linear relationship with the frequency deviation. For emergency standby demand, a hot standby mode is set to maintain the battery state of charge between 90% and 95%. When a start command is received, the rated power output is reached within 200 milliseconds. A priority arbitration mechanism is set between the strategies, with emergency standby having the highest priority, followed by frequency regulation, and peak-shaving having the lowest priority, while ensuring that only one control strategy is executed at a time.

[0051] The control decision module is configured under the frequency regulation strategy to: calculate the required power compensation amount based on the frequency deviation value and frequency change rate in the load demand signal; generate a dynamically changing power setpoint based on the required power compensation amount and the battery state of charge; and adjust the power output of the battery storage system through the power regulation module.

[0052] The frequency regulation control employs a feedforward-feedback composite control structure. The feedforward section predicts the power deficit based on the frequency change rate and calculates the anticipatory power command using the gradient descent method. The feedback section uses adaptive proportional-integral control, with the proportional coefficient automatically adjusted based on the system inertia constant. The power compensation calculation incorporates an adaptive battery state-of-charge (SOC) adjustment mechanism: when the SOC is below 50%, the discharge compensation power is gradually reduced; when it is above 90%, the charging compensation power is gradually reduced. The power setpoint generation uses a sliding mode control algorithm, calculating the power command in each control cycle (100 milliseconds) and ensuring power changes remain within the battery's allowable range through a limiting module. A power reserve management function is also implemented, dynamically allocating power reserves with different response speeds according to frequency regulation requirements: 20% for primary frequency regulation (second-level), 30% for secondary frequency regulation (minute-level), and the remaining capacity is used for peak shaving applications. All frequency regulation parameters comply with the grid frequency regulation technical requirements standards.

[0053] The control system also includes an operation log module, which records historical operating parameters collected by the status monitoring module and historical instruction data generated by the control decision module, and generates a battery performance degradation trend report based on the historical data.

[0054] The operation log module uses a time-series database to store data, with a configurable recording interval (default 1 minute) and storage capacity supporting 10 years of local storage of operational data. Recorded data includes over 50 parameters such as battery cell voltage, current, temperature, state of charge, health indicators, charge / discharge power commands, grid frequency, and load demand. Data compression employs a lossless compression algorithm, achieving a compression ratio of 1:5. Performance degradation analysis uses a multiple linear regression model, with cycle number, operating temperature, and average depth of discharge as independent variables and capacity decay rate as the dependent variable, to establish a degradation trend prediction equation. A performance report is generated quarterly, including key indicators such as capacity decay curves, internal resistance growth trends, and changes in maximum power capability. A data export interface is also provided, supporting CSV and JSON formats for subsequent in-depth analysis. All data records are time-stamped with a time synchronization accuracy of 1 millisecond.

[0055] The load demand interface is also configured to receive output forecast information from renewable energy power generation equipment, and the control decision module is further configured to generate pre-charge or pre-discharge instructions in advance based on the comparison results between the output forecast information and the real-time load demand signal, so as to smooth the output fluctuations of renewable energy.

[0056] The load demand interface has been expanded to support renewable energy forecast data access. The forecast data includes photovoltaic / wind power forecast curves with a time resolution of 15 minutes and a forecast duration covering 24 hours. The forecast data format adopts the CIM / E standard format and is updated hourly via file transfer protocol. The control decision module establishes a multi-timescale coordinated control architecture: in the day-ahead phase, a charging and discharging plan is formulated based on the forecast data, using a stochastic optimization algorithm to account for forecast uncertainties; in the hourly phase, the plan is rolled back based on ultra-short-term forecasts; in the real-time phase, model predictive control is used, optimizing instructions every 5 minutes. Smoothing control employs a low-pass filter principle, setting an adjustable smoothing time constant (1-30 minutes) to absorb renewable energy fluctuations through battery charging and discharging. Simultaneously, protection logic is implemented: when the actual power deviation from the forecast exceeds 30% for 15 consecutive minutes, the system automatically switches to traditional control mode to prevent system anomalies caused by forecast errors.

[0057] The power regulation module includes a bidirectional converter and a control circuit connected thereto. The control circuit is configured to receive the power setpoint and control the conduction state of the bidirectional converter through pulse width modulation to achieve continuous regulation of the charging and discharging power of the battery storage system.

[0058] The power regulation module adopts a three-stage power conversion structure: a DC / DC converter for battery-side voltage regulation, a DC / AC converter for grid-connected inversion, and a filter circuit to meet harmonic requirements. The control circuit uses a dual-closed-loop control structure: an outer power loop with a 1-millisecond sampling period, and an inner current loop with a 100-microsecond sampling period. Pulse width modulation uses voltage space vector modulation technology with a switching frequency of 10kHz, achieving four-quadrant operation by adjusting the modulation ratio and phase angle. Power control accuracy reaches ±1% of the rated value, with a dynamic response time of less than 50 milliseconds. Protection functions include overcurrent protection (100-microsecond action time), overvoltage protection, and islanding protection. The module uses forced air cooling, with temperature monitoring points located on the power device heatsinks; it automatically derating when the temperature exceeds 75℃. The module supports hot-swapping for easy maintenance and expansion.

[0059] The control decision module generates a stop discharge command when the battery state of charge is below the minimum protection threshold and a stop charging command when the battery state of charge is above the maximum protection threshold. The priority of the command is higher than other load demand response commands.

[0060] The protection threshold settings employ an adaptive adjustment strategy: the minimum protection threshold is dynamically adjusted based on the battery's health status, ranging from 10% to 20%, with lower thresholds for better health; the maximum protection threshold is set between 90% and 95%, and considering the effect of temperature, the threshold decreases by 2% for every 10°C increase in temperature. Protection commands are generated using hardware priority circuitry to ensure priority execution under all circumstances. When the state of charge (SBC) approaches the protection threshold, a gradual power reduction strategy is adopted: power is linearly reduced starting at 5% of the threshold and decreasing to zero at the threshold. Protection recovery utilizes hysteresis characteristics: after discharge protection, the SBC must recover to 3% above the threshold before protection is released; after charging protection, the SBC must drop to 2% below the threshold before protection is released. All protection actions are recorded in the event log, including detailed information such as action time, pre-action state, and action reason.

[0061] The control system also includes an alarm module, which generates a visual or audible alarm signal when the operating parameters exceed the safe range, and simultaneously sends a command to the control decision module to trigger the system to enter a protective charging and discharging mode.

[0062] The alarm module employs a multi-level alarm system: early warning (parameters approaching limits), general alarm (parameters exceeding limits but automatically recovering), and severe alarm (requiring manual intervention). Alarm conditions include: voltage over-limit (±10%), temperature over-limit (0-45℃), current over-limit (±150% of rated value), and communication interruption. Visual alarms utilize a combination of three-color indicator lights and an LCD display, while audible alarms use programmable voice prompts. Protective charging and discharging modes include: power limiting mode (power reduced to 50%), pause mode (standby mode), and safe shutdown mode (complete disconnection). Alarm information is simultaneously uploaded to the monitoring center via the communication interface, supporting multiple notification methods such as SMS and email. Alarm history records are stored for at least 3 years and can be queried and statistically analyzed by time, type, and level. All alarm settings provide debugging interfaces for easy on-site debugging and maintenance.

[0063] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change. Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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. An energy storage control system, applied to an energy storage device including a battery storage system, characterized in that, The control system includes: The status monitoring module is used to collect the operating parameters of the battery storage system in real time, including the battery state of charge, charging and discharging current and battery temperature. The load demand interface is used to receive load demand signals sent by the external power grid. The control decision module, connected to the status monitoring module and the load demand interface, is configured to generate charging and discharging instructions based on the load demand signal and the operating parameters. The charging and discharging instructions include a power setting value and a time parameter for charging or discharging. A power regulation module, connected to the control decision module and the battery storage system, is configured to adjust the charging and discharging power of the battery storage system according to the charging and discharging command, so as to match the grid load demand.

2. The energy storage control system according to claim 1, characterized in that, The status monitoring module is also used to collect battery health status parameters, and the control decision module is further configured to: when generating the charge and discharge command, compare the battery health status parameters with a preset threshold, and limit the absolute value of the charge and discharge power according to the comparison result, so as to keep the battery health status parameters within a preset range.

3. The energy storage control system according to claim 2, characterized in that, The control decision module is configured to perform the following operations: When the battery health status parameter indicates that the battery aging degree exceeds the first threshold, the absolute value of the maximum allowable charge and discharge power will be reduced from the rated value to the first preset value. When the battery health status parameter indicates that the battery internal resistance exceeds the second threshold, the adjustment rate of the charging and discharging power is limited to below the second preset value.

4. The energy storage control system according to claim 1, characterized in that, The control decision module is configured as follows: Analyze the load demand signal to identify the type of power grid demand, such as peak shaving, frequency regulation, or emergency backup. Select the corresponding charging and discharging control strategy according to different demand types. The peak shaving strategy corresponds to continuous charging and discharging power control, the frequency modulation strategy corresponds to fast power response control at the second or minute level, and the emergency backup strategy corresponds to instantaneous power start-up control in standby mode.

5. The energy storage control system according to claim 4, characterized in that, The control decision module is configured under the frequency modulation strategy as follows: Calculate the required power compensation based on the frequency deviation and frequency change rate in the load demand signal; Based on the required power compensation amount and the battery state of charge, a dynamically changing power setpoint is generated, and the power output of the battery storage system is adjusted through the power regulation module.

6. The energy storage control system according to claim 1, characterized in that, The control system also includes an operation log module, which records historical operating parameters collected by the status monitoring module and historical instruction data generated by the control decision module, and generates a battery performance degradation trend report based on the historical data.

7. The energy storage control system according to claim 1, characterized in that, The load demand interface is also configured to receive output forecast information from renewable energy power generation equipment, and the control decision module is further configured to generate pre-charge or pre-discharge instructions in advance based on the comparison results between the output forecast information and the real-time load demand signal, so as to smooth the output fluctuations of renewable energy.

8. The energy storage control system according to claim 1, characterized in that, The power regulation module includes a bidirectional converter and a control circuit connected thereto. The control circuit is configured to receive the power setpoint and control the conduction state of the bidirectional converter through pulse width modulation to achieve continuous regulation of the charging and discharging power of the battery storage system.

9. The energy storage control system according to claim 1, characterized in that, The control decision module generates a stop discharge command when the battery state of charge is below the minimum protection threshold and a stop charging command when the battery state of charge is above the maximum protection threshold. The priority of the command is higher than other load demand response commands.

10. The energy storage control system according to claim 1, characterized in that, The control system also includes an alarm module, which generates a visual or audible alarm signal when the operating parameters exceed the safe range, and simultaneously sends a command to the control decision module to trigger the system to enter a protective charging and discharging mode.

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