Energy management system and method for energy storage system

Abstract

The invention discloses an energy management system and method of an energy storage system, relates to the technical field of energy management of the energy storage system, and solves the problems of incomplete data collection, slow control feedback and uneven energy resource management and scheduling of energy storage equipment. The system comprises a monitoring acquisition module, a communication module, a control module, a battery management module, an energy management module, a safety protection module and a man-machine interaction module. According to the invention, the sensor network is arranged to monitor and collect the working data of the energy storage equipment in real time; accurate control of the energy management system is realized through modularized management of the battery pack and the energy; the supply and demand balance of the system is calculated and analyzed through a prediction algorithm and a scheduling algorithm, energy is reasonably distributed, the problem of uneven energy resource management and scheduling is solved, and the safe and stable operation capability of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and more specifically to an energy management system and method for an energy storage system. Background Art

[0002] With the continuous growth of global energy demand and the transformation of energy structure, users have put forward higher requirements for the stability and power supply quality of the power grid. However, the existing energy management technology of energy storage system still has problems such as incomplete data collection of energy storage equipment, slow control feedback, and uneven energy resource management and scheduling.

[0003] In the existing technology, the problem of incomplete data collection of energy storage equipment has been well solved, and the data of the operation and working status of energy storage equipment has been fully collected to provide strong data support for system control. However, there are still problems such as slow control feedback and uneven energy resource management and scheduling that have not been solved.

[0004] To solve the above problems, the present invention achieves precise control of energy storage equipment by setting up regional power management and energy management, thereby solving the problem of slow system control feedback; calculates and analyzes the supply and demand balance of the system through prediction algorithms and scheduling algorithms, and reasonably distributes energy to solve the problem of uneven energy resource management and scheduling. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention discloses an energy management system and method for an energy storage system. By setting up a sensor network, the working data of the energy storage equipment is monitored and collected in real time. The energy management system is precisely controlled by modularizing the battery pack and energy. The supply and demand balance of the system is calculated and analyzed through a prediction algorithm and a scheduling algorithm, and the energy is reasonably distributed to solve the problem of uneven energy resource management and scheduling.

[0006] The present invention adopts the following technical solutions:

[0007] An energy storage system energy management system and method, characterized by comprising a monitoring and acquisition module, a communication module, a control module, a battery management module, an energy management module, a safety protection module and a human-computer interaction module; wherein:

[0008] A monitoring and acquisition module monitors and acquires working status information of the energy storage device in real time through a sensor network, wherein the working status information includes at least current, voltage, temperature and humidity;

[0009] A communication module, used to realize external information interaction between the power grid, renewable energy power generation system and load management system, the communication module includes a communication controller and a communication interface connected to the communication controller, a network protocol identification module, a network conversion device and an information sending and receiving prompt module;

[0010] A control module receives data from the communication module based on a multi-level control system, wherein the multi-level control system includes a charge and discharge control system, an inverter control system, an energy storage control system, and an energy storage system coordination control system; the output end of the charge and discharge control system is connected to the input end of the inverter control system; the output end of the inverter control system is connected to the input end of the energy storage control system; the output end of the energy storage control system is connected to the input end of the energy storage system coordination control system;

[0011] A battery management module is used to manage energy storage and energy storage status. The battery management module includes a battery status detection module, a charge and discharge balance control module, an overcurrent protection module, a charge and discharge status evaluation module and an energy storage status display module; the output end of the battery status detection module is connected to the input end of the charge and discharge balance control module; the output end of the charge and discharge balance control module is connected to the input end of the overcurrent protection module; the output end of the overcurrent protection module is connected to the input end of the charge and discharge status evaluation module;

[0012] The energy management module includes an energy optimization unit, a supply and demand analysis unit, an energy conversion unit and an energy scheduling unit. The supply and demand analysis unit predicts energy demand through a prediction algorithm, and the energy scheduling unit comprehensively manages energy in the energy storage device through a scheduling algorithm; the output end of the energy optimization unit is connected to the input end of the energy conversion unit; the output end of the supply and demand analysis unit is connected to the input end of the energy conversion unit; the output end of the energy conversion unit is connected to the input end of the energy scheduling unit;

[0013] A safety protection module, based on the data information of the monitoring and acquisition module and the rated threshold information, uses a circuit breaker and an overload protector to cut off the power supply when the energy storage device works abnormally, so as to ensure the safety of the energy storage device;

[0014] The human-computer interaction module displays the status information of the energy storage system to the operator in real time through the display screen, and allows the operator to adjust the system operation through the control panel;

[0015] The output end of the monitoring and acquisition module is connected to the input end of the communication module; the input end of the communication module is connected to the output end of the control module; the output end of the control module is connected to the input ends of the battery management module, the energy management module and the human-computer interaction module; the output end of the safety protection module is connected to the input ends of the monitoring and acquisition module, the battery management module and the energy management module; the output end of the human-computer interaction module is connected to the input ends of the control module and the energy management module.

[0016] As a further technical solution of the present invention, the sensor network includes a variable frequency power sensor, a temperature sensor and a humidity sensor; the variable frequency power sensor measures the fundamental and harmonic waves of the variable frequency electricity of the energy storage system and performs signal conversion and Fourier change calculation on it, and then directly outputs current and voltage values; the temperature sensor monitors the temperature changes of the energy storage device and the battery pack in real time based on thermistors; the humidity sensor monitors the changes in the content of water molecules in the energy storage device, the battery pack and the atmospheric environment based on nano gas sensors, and then monitors the changes in humidity; the data monitored by the sensor is transmitted to the control module in real time through the communication module and the data is saved.

[0017] As a further technical solution of the present invention, the network protocol identification module performs protocol parsing and identification on the communication protocol through a bidirectional linked list and a parsed data queue of a data group; the network conversion device converts energy data through a converter, and converts the analog signals of the data information of the power grid, renewable energy power generation system and load management system into digital signals to ensure the uniformity of the units of the data volume for easy transmission; the information transceiver prompt module sends and receives text messages indicating successful signal transmission through the STM32F103C8 chip; the output end of the network protocol identification module is connected to the input end of the network conversion device and the information transceiver prompt module.

[0018] As a further technical solution of the present invention, the battery charge and discharge control system controls the charge and discharge current, charge and discharge voltage and charge and discharge cut-off amount of the energy storage device through a bidirectional converter, and implements a two-charge and two-discharge charge and discharge method to improve energy utilization; the inverter control system controls the inverter of the energy storage device through a voltage start-up circuit and a three-phase closed-loop control circuit; the energy storage control system monitors the maximum storage capacity and current remaining amount of energy of the battery of the energy storage device in real time, and transmits a signal to the battery charge and discharge control system to stop charging when the energy reaches the maximum value, and transmits a signal to the battery charge and discharge control system to charge immediately when the energy is lower than the minimum storage value and the energy drops sharply; the energy storage system coordination control system balances the production and consumption of energy through the proportional relationship between the consumed energy and the reserved energy, and analyzes the scheduling method of the state of the energy storage device through the energy control method to control the input and output of energy.

[0019] As a further technical solution of the present invention, the implementation steps of the energy control method are:

[0020] 1) Set the system charge and discharge parameters and DC side battery system parameters according to the rated working data of the energy storage equipment, where the nominal capacity is 235KWh, the rated charge and discharge rate is 0.5P / 0.5P, the nominal voltage is 832V, the working voltage range is 728~949V, the rated power in the PSC parameters is 100KW*1, and the rated voltage on the AC side is 400V;

[0021] 2) Start the charging and discharging process of the energy storage device by sending instructions to the battery management module, and monitor the battery status and feed it back to the main control unit of the control module;

[0022] 3) Based on the working data fed back by the energy management module and the real-time energy demand, the charging and discharging power of the energy storage device is adjusted to meet the needs of the power grid and users;

[0023] 4) Based on the prediction of time and energy by the prediction algorithm, the energy demand is obtained, and the charging and discharging time of the system equipment is controlled according to the actual demand, charging during peak hours and discharging during off-peak hours to achieve efficient use of electrical energy.

[0024] As a further technical solution of the present invention, the energy optimization unit optimizes the design and control of the energy system of the energy storage device based on the actual charging and discharging requirements and time of the system and the user's energy usage requirements, and obtains the optimal energy usage plan through in-depth analysis of energy usage and energy storage; the supply and demand analysis unit analyzes the supply and demand balance of the system and predicts future energy supply and demand through a prediction algorithm based on the energy data and historical energy data of the monitoring and acquisition module; the energy conversion unit converts alternating current into direct current through a frequency converter, and converts thermal energy into electrical energy through a heat exchanger to achieve efficient use of energy; the energy scheduling unit uniformly schedules and manages the system energy based on the data provided by the supply and demand analysis unit, and uses a scheduling algorithm to reasonably allocate and efficiently utilize energy.

[0025] An energy management method for an energy storage system, characterized in that the method comprises the following steps:

[0026] Step 1: Collect the operating data of each device in the energy storage system in real time through sensors and communication interfaces, including at least voltage, current, temperature, state of charge and health status;

[0027] Step 2: Based on the collected data information and historical data, the energy supply and demand of the energy storage system is analyzed through the prediction algorithm;

[0028] Step 3: Optimize the energy usage of the energy storage system based on the analysis and prediction results, and determine the charging and discharging planning curve;

[0029] Step 4: Based on the optimization results, the charging and discharging power of the battery pack is controlled through the scheduling algorithm and the energy usage is allocated;

[0030] Step 5: Monitor the operating status of the energy storage system and promptly notify the operator when the system collected data exceeds the threshold or a fault occurs;

[0031] Step 6: Generate tables and curves of the operating status of the energy storage system and energy management system, and timely analyze and feedback the system adjustment parameters to ensure efficient operation of the system.

[0032] As a further technical solution of the present invention, the implementation steps of the prediction algorithm are:

[0033] 1) Data collection and comparison;

[0034] Collect system real-time monitoring data and system historical operation data, and compare the two sets of data to find similarities and differences;

[0035] 2) Supply and demand analysis;

[0036] Based on the analyzed data and the similarities between the two sets of data, the supply and demand requirements of the system energy are summarized;

[0037] 3) Time series forecasting;

[0038] The data is combined according to the time series, and the time data is used to predict the energy usage. The expression of the prediction algorithm is:

[0039]

[0040] In formula (1), X represents the horizontal calculation of data, Y represents the vertical calculation of data, Q represents the output value of the prediction result, α represents the horizontal time prediction parameter, β represents the trend time prediction parameter, and x i Indicates the current operation data, x i-1 Indicates the data of the previous moment. Represents the regression value of the operation data, y i Represents historical operation data, y i-1 Represents data at a certain moment in history. represents the regression value of historical operation data, i represents the operation order, n represents the number of operations, t represents the prediction period, k represents the ideal time data, δ represents the prediction operator, μ represents the cycle period, and di represents the integral sign;

[0041] 4) Exponential smoothing;

[0042] Further smoothing calculation of the predicted data to ensure data availability;

[0043] P = αx i +(1-α)x i-1 +hx i (2)

[0044] In formula (2), P represents the smoothing calculation of the prediction index, α represents the time prediction parameter, and x i Represents the operation data, and h represents the prediction step length.

[0045] As a further technical solution of the present invention, the implementation steps of the scheduling algorithm are:

[0046] 1) Determine priorities;

[0047] Determine the priority of energy scheduling from high to low, and adjust the priority in real time as the process progresses;

[0048] 2) Real-time scheduling;

[0049] Dispatch energy storage equipment based on real-time data to ensure stable operation of the system;

[0050]

[0051] In formula (3), F represents the real-time scheduling output value, a, b, c represent the priority of energy level, i represents the operation order, m represents the number of operations, A max Indicates the highest priority of the operation order, A all represents all energy levels, α represents the data heuristic value weight, and β represents the expected heuristic value weight;

[0052] 3) Predictive scheduling;

[0053] Based on the forecasted supply and demand results, energy storage equipment is scheduled in advance;

[0054]

[0055] In formula (4), G represents the predicted scheduling output value, X, Y, Q represent the priority of the predicted energy level, i represents the operation order, m represents the number of operations, and A max Indicates the highest priority of the operation order, A all represents all energy levels, α represents the data heuristic value weight, and β represents the expected heuristic value weight;

[0056] 4) Emergency dispatch;

[0057] Rapidly adjust the dispatch strategy in emergency situations to ensure the system's emergency response speed;

[0058]

[0059] In formula (5), F represents the real-time scheduling output value, a and b represent the priority of energy level, ρ represents burst data, i represents the operation order, m represents the number of operations, and A max Indicates the highest priority of the operation order, A all represents all energy levels, α represents the data heuristic value weight, β represents the expected heuristic value weight, and Q represents the burst state parameter.

[0060] Compared with the prior art, the present invention has the following beneficial positive effects:

[0061] The present invention sets up a sensor network to monitor and collect the working data of the energy storage equipment in real time; realizes precise control of the energy management system by modularizing the management of battery packs and energy; calculates and analyzes the supply and demand balance of the system through prediction algorithms and scheduling algorithms, and reasonably distributes energy to solve the problem of uneven energy resource management and scheduling, so as to ensure safe and stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0063] Figure 1 A schematic diagram of the overall architecture of an energy management system for an energy storage system according to the present invention;

[0064] Figure 2 A flow chart of an energy storage system energy management system method of the present invention;

[0065] Figure 3 An inverter three-phase closed-loop control circuit of an inverter control system of a control module of an energy management system of an energy storage system of the present invention;

[0066] Figure 4 A schematic diagram of the internal architecture of an energy management module of an energy management system of an energy storage system of the present invention;

[0067] Figure 5 The present invention is a flowchart of an implementation of a prediction algorithm for an energy management method for an energy storage system. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of this article to clearly and completely describe the technical solutions in the embodiments of this article. Obviously, the described embodiments are only part of the embodiments of this article, not all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0069] like Figure 1 As shown, an energy storage system energy management system includes a monitoring and acquisition module, a communication module, a control module, a battery management module, an energy management module, a safety protection module and a human-computer interaction module; wherein:

[0070] A monitoring and acquisition module monitors and collects the working status information of the energy storage device in real time through a sensor network, including at least current, voltage, temperature and humidity;

[0071] A communication module, used to realize external information interaction between the power grid, renewable energy power generation system and load management system, the communication module includes a communication controller and a communication interface connected to the communication controller, a network protocol identification module, a network conversion device and an information sending and receiving prompt module;

[0072] A control module receives data from the communication module based on a multi-level control system, wherein the multi-level control system includes a charge and discharge control system, an inverter control system, an energy storage control system, and an energy storage system coordination control system; the output end of the charge and discharge control system is connected to the input end of the inverter control system; the output end of the inverter control system is connected to the input end of the energy storage control system; the output end of the energy storage control system is connected to the input end of the energy storage system coordination control system;

[0073] A battery management module is used to manage energy storage and energy storage status. The battery management module includes a battery status detection module, a charge and discharge balance control module, an overcurrent protection module, a charge and discharge status evaluation module and an energy storage status display module; the output end of the battery status detection module is connected to the input end of the charge and discharge balance control module; the output end of the charge and discharge balance control module is connected to the input end of the overcurrent protection module; the output end of the overcurrent protection module is connected to the input end of the charge and discharge status evaluation module;

[0074] The energy management module includes an energy optimization unit, a supply and demand analysis unit, an energy conversion unit and an energy scheduling unit. The supply and demand analysis unit predicts energy demand through a prediction algorithm, and the energy scheduling unit comprehensively manages energy in the energy storage device through a scheduling algorithm; the output end of the energy optimization unit is connected to the input end of the energy conversion unit; the output end of the supply and demand analysis unit is connected to the input end of the energy conversion unit; the output end of the energy conversion unit is connected to the input end of the energy scheduling unit;

[0075] A safety protection module, based on the data information of the monitoring and acquisition module and the rated threshold information, uses a circuit breaker and an overload protector to cut off the power supply when the energy storage device works abnormally, so as to ensure the safety of the energy storage device;

[0076] The human-computer interaction module displays the status information of the energy storage system to the operator in real time through the display screen, and allows the operator to adjust the system operation through the control panel;

[0077] The output end of the monitoring and acquisition module is connected to the input end of the communication module; the input end of the communication module is connected to the output end of the control module; the output end of the control module is connected to the input ends of the battery management module, the energy management module and the human-computer interaction module; the output end of the safety protection module is connected to the input ends of the monitoring and acquisition module, the battery management module and the energy management module; the output end of the human-computer interaction module is connected to the input ends of the control module and the energy management module.

[0078] Furthermore, the sensor network includes a variable frequency power sensor, a temperature sensor and a humidity sensor; the variable frequency power sensor measures the fundamental and harmonic waves of the variable frequency power of the energy storage system and performs signal conversion and Fourier change calculation on it, thereby directly outputting current and voltage values; the temperature sensor monitors the temperature changes of the energy storage device and the battery pack in real time based on thermistors; the humidity sensor monitors the changes in the content of water molecules in the energy storage device, the battery pack and the atmospheric environment based on nano gas sensors, thereby monitoring the changes in humidity; the data monitored by the sensor is transmitted to the control module in real time through the communication module and the data is saved.

[0079] In a specific embodiment, the variable frequency power sensor performs AC sampling of the input voltage and current signals through high-precision sampling technology, and sends the sampled values ​​to the digital input secondary instrument through a transmission system such as a cable or optical fiber. These secondary instruments then perform mathematical operations on the sampled values ​​to obtain key parameters such as voltage effective value, current effective value, fundamental voltage, fundamental current, harmonic voltage, harmonic current, active power, fundamental power and harmonic power, and save and transmit the data of voltage effective value and current effective value to the control module for operation. The temperature sensor measures the temperature by detecting the change in resistance value caused by temperature change; when the temperature of the energy storage device and the battery pack changes, the resistance value of the thermistor will change accordingly, and this change is converted into an electrical signal and output; the thermistor has high sensitivity, good linearity and stability to ensure that the temperature change can be accurately reflected in different temperature ranges; it also needs to have fast response capability and anti-interference ability to adapt to the use requirements of energy storage devices and battery packs in complex environments such as high temperature, low temperature or rapid temperature change. The nano gas sensor is composed of zero-dimensional metal oxide semiconductor nanoparticles, carbon nanotubes and two-dimensional nanofilms, which effectively improves the sensitivity and working performance of the sensor.

[0080] Furthermore, the network protocol identification module performs protocol parsing and identification on the communication protocol through a bidirectional linked list and a parsed data queue of the data group; the network conversion device converts the energy data through a converter, and converts the analog signals of the data information of the power grid, renewable energy power generation system and load management system into digital signals to ensure the uniformity of the unit of data volume for easy transmission; the information transceiver prompt module sends and receives text messages indicating successful signal transmission through the STM32F103C8 chip; the output end of the network protocol identification module is connected to the input end of the network conversion device and the information transceiver prompt module.

[0081] In a specific embodiment, the communication interface is based on the CAN bus to physically connect the internal sensors of the monitoring acquisition module and the external connections of the control module, battery management module and energy management module to ensure that the signal can be transmitted stably; the internal communication defines the format, rate and verification method of data transmission through the SPI communication protocol, and stipulates the interaction mode and response mechanism between modules to ensure efficient and accurate data transmission. The STM32F103C8 chip runs between 3.4V and 4.4V through the serial interface modem of the GSM module, performs remote data transmission through the RS232 serial protocol, and receives and sends voice calls and text messages with low power consumption.

[0082] Furthermore, the battery charge and discharge control system controls the charge and discharge current, charge and discharge voltage and charge and discharge cut-off amount of the energy storage device through a bidirectional converter, and implements a two-charge and two-discharge charge and discharge method to improve energy utilization; the inverter control system controls the inverter of the energy storage device through a voltage start loop and a three-phase closed-loop control loop; the energy storage control system monitors the maximum storage capacity and current remaining amount of energy of the battery of the energy storage device in real time, and transmits a signal to the battery charge and discharge control system to stop charging when the energy reaches the maximum value, and transmits a signal to the battery charge and discharge control system to charge immediately when the energy is lower than the minimum storage value and the energy drops sharply; the energy storage system coordination control system balances the production and consumption of energy through the proportional relationship between consumed energy and reserved energy, and analyzes the scheduling method of the state of the energy storage device through an energy control method to control the input and output of energy.

[0083] In a specific embodiment, the two-charge and two-discharge charging and discharging method is: in a complete charging and discharging cycle, the energy storage power station will perform two charges and two discharges. Through multiple charges and discharges, the grid load is balanced and the efficient use of energy is improved. Multiple charges ensure that the released electric energy can meet the peak power demand, better cope with grid frequency regulation and peak-valley balance, and optimize the power supply structure through multiple discharges, reduce grid pressure and reduce energy consumption. The voltage start-up loop means: when the input DC voltage reaches a preset value, the inverter starts working and converts the DC voltage into an AC voltage output; the voltage start-up loop realizes the protection of the energy storage device, ensuring that the circuit works normally and is not burned when the input voltage is too large; the closed-loop control loop realizes the decoupling control of the inverter, eliminating the influence of disturbances on the grid voltage and current feedback.

[0084] Furthermore, the energy control method is implemented by:

[0085] 1) Set the system charge and discharge parameters and DC side battery system parameters according to the rated working data of the energy storage equipment, where the nominal capacity is 235KWh, the rated charge and discharge rate is 0.5P / 0.5P, the nominal voltage is 832V, the working voltage range is 728~949V, the rated power in the PSC parameters is 100KW*1, and the rated voltage on the AC side is 400V;

[0086] 2) Start the charging and discharging process of the energy storage device by sending instructions to the battery management module, and monitor the battery status and feed it back to the main control unit of the control module;

[0087] 3) Based on the working data fed back by the energy management module and the real-time energy demand, the charging and discharging power of the energy storage device is adjusted to meet the needs of the power grid and users;

[0088] 4) Based on the prediction of time and energy by the prediction algorithm, the energy demand is obtained, and the charging and discharging time of the system equipment is controlled according to the actual demand, charging during peak hours and discharging during off-peak hours to achieve efficient use of electrical energy.

[0089] In a specific embodiment, the current and voltage parameter settings need to be based on the technical characteristics and safety requirements of the battery to ensure the safety and efficiency of the charging and discharging process. At the same time, the operating temperature range of the energy storage device should be: -30°C ~ 55°C. At this temperature, the energy storage device can maintain a normal and efficient working state. The charging and discharging time needs to be dynamically adjusted according to the compliance of the power grid and energy price factors to ensure the maximum economic benefits of the energy storage system. During the charging and discharging process, the sensor network monitors the operating status in real time to ensure the safety and stability of the process. After the charging and discharging process, the results are evaluated and analyzed, including the battery's charging status, discharge capacity, efficiency and other indicators to optimize the subsequent control strategy. When the charging and discharging process is completed and the set conditions are met, the charging and discharging operations must be stopped in time to ensure the battery's operating life and safety.

[0090] Furthermore, the energy optimization unit optimizes the design and control of the energy system of the energy storage device based on the actual charging and discharging requirements and time of the system and the user's energy usage requirements, and obtains the optimal energy usage plan through in-depth analysis of energy usage and energy storage; the supply and demand analysis unit analyzes the supply and demand balance of the system and predicts future energy supply and demand through a prediction algorithm based on the energy data and historical energy data of the monitoring and acquisition module; the energy conversion unit converts AC power into DC power through a frequency converter, and converts thermal energy into electrical energy through a heat exchanger to achieve efficient energy utilization; the energy scheduling unit uniformly schedules and manages the system energy based on the data provided by the supply and demand analysis unit, and uses a scheduling algorithm to reasonably allocate and efficiently utilize energy.

[0091] In a specific embodiment, the optimization design of the energy optimization unit includes optimizing energy conversion efficiency, energy storage efficiency, energy loss and system operation cost, establishing a mathematical model of the optimization problem by using linear programming, selecting the optimization method of the best operation efficiency of the current energy management system, and obtaining the linear optimal solution by computer to obtain the appropriate charging and discharging strategy and energy storage method of the energy management system. The supply and demand analysis unit models the real-time data and historical data of the energy supply and demand data relationship by data statistics method, and uses the prediction algorithm to accurately analyze the energy use of the system, predict the energy supply and demand trend in the future, and provide strong data support for system scheduling; the prediction algorithm is used to add the dynamic changes of real-time data on the basis of historical data, enriching the multidimensionality of the data and ensuring the accuracy of the prediction results. The frequency converter converts AC into DC through a rectifier, converts the tributary into AC through an inverter, adjusts the voltage and frequency of the output power supply through the opening and closing of the transistor, and provides the power supply voltage through the smoothing circuit to achieve the purpose of energy saving and speed regulation; the working principle of the heat exchanger is: the heat in the primary line is transferred to the secondary line through the internal metal wall, so that the water in the secondary line is heated to the required temperature, and at the same time follows the law of conservation of energy. The heat insulation layer device can ensure the maximum heat conversion efficiency and reduce heat loss, thus avoiding the waste of resources.

[0092] Furthermore, the safety protection module cuts off and connects the load circuit of the energy storage device through the circuit breaker, and performs overcurrent protection, short circuit protection, phase loss protection, phase imbalance protection and phase mismatch protection on the circuit of the energy storage device through the overload protector.

[0093] In a specific implementation example, the circuit breaker uses a mutual inductor to collect the current size of each phase, and uses the generated magnetic field to overcome the path mode of the circuit to trip it, thereby protecting the safety of the circuit in sudden and emergency situations. The overload protector samples the signal through the current mutual inductor, converts the load current of each phase into a voltage signal proportional to it, continuously collects and calculates the signal and all control signals, and compares them with the current and voltage values ​​of the normal operation of the energy storage device, and outputs the operation results. The overcurrent protection sets a limit on the time that a large current flows through, and disconnects the current when the time limit is exceeded, and can be reset in 5 minutes. The short-circuit protection is a short circuit when the ratio of the load current to the current setting value exceeds 2.7, and the current is disconnected within 0.5s. The phase loss protection is a short circuit when the current value of any phase of the three phases of the circuit is lower than the current setting value. The current is disconnected and cut off immediately. The phase imbalance protection is to automatically disconnect the current when the three-phase current imbalance of the circuit is 50%. The phase mismatch protection is that when the phase sequence of the three-phase power supply of the circuit is opposite to the original phase sequence, the overload protector disconnects the current within 0.2s.

[0094] The present invention also adopts the following technical solutions:

[0095] An energy management method for an energy storage system comprises the following steps:

[0096] Step 1: Collect the operating data of each device in the energy storage system in real time through sensors and communication interfaces, including at least voltage, current, temperature, state of charge and health status;

[0097] Step 2: Based on the collected data information and historical data, the energy supply and demand of the energy storage system is analyzed through the prediction algorithm;

[0098] Step 3: Optimize the energy usage of the energy storage system based on the analysis and prediction results, and determine the charging and discharging planning curve;

[0099] Step 4: Based on the optimization results, the charging and discharging power of the battery pack is controlled through the scheduling algorithm and the energy usage is allocated;

[0100] Step 5: Monitor the operating status of the energy storage system and promptly notify the operator when the system collected data exceeds the threshold or a fault occurs;

[0101] Step 6: Generate tables and curves of the operating status of the energy storage system and energy management system, and timely analyze and feedback the system adjustment parameters to ensure efficient operation of the system.

[0102] Furthermore, the implementation steps of the prediction algorithm are:

[0103] 1) Data collection and comparison;

[0104] Collect the real-time monitoring data of the system and the historical operation data of the system, and compare the two sets of data to find similarities and differences; in a specific embodiment, the data in the operation of the system is collected in real time through sensors, monitoring cameras, data collectors, etc., and the data types include temperature, pressure, flow, vibration, current, voltage, equipment status, etc. Extract the past operation data of the system from databases, log files, and historical records. Whether the real-time data is consistent with the historical data in trend, such as the temperature change trend. Whether the real-time data matches the specific pattern or event in the historical data. Whether the real-time data reaches the performance indicator range in the historical data, such as the equipment operation efficiency. Whether there are abnormal conditions in the real-time data that are not in the historical data, such as a sudden increase in temperature. Whether the rate of change of the real-time data is different from that of the historical data, indicating that the equipment is aging or faulty. Whether the frequency distribution of the real-time data is significantly different from that of the historical data, reflecting the change of the operating conditions. Whether the duration of the abnormality or specific event in the real-time data is different from that of the historical data. Systematically compare the real-time monitoring data and the historical operation data to find the similarities and differences of the system, and provide a basis for the maintenance, optimization and prediction of the system.

[0105] 2) Supply and demand analysis;

[0106] Based on the analyzed data and the similarities between the two sets of data, the supply and demand requirements of the system energy are summarized;

[0107] 3) Time series forecasting;

[0108] The data is combined according to the time series, and the time data is used to predict the energy usage. The expression of the prediction algorithm is:

[0109]

[0110] In formula (1), X represents the horizontal calculation of data, which is used to evaluate the horizontal change or correlation of data.

[0111] Y represents data longitudinal calculation, which is a series of calculations performed on historical data to analyze data trends or patterns.

[0112] Q represents the output value of the prediction result, which is the final result of the model prediction. α represents the horizontal time prediction parameter, which is used to adjust the weight in the horizontal calculation and affects the sensitivity of the model to changes in the current data.

[0113] β represents the trend time prediction parameter, which is used to adjust the weight in the longitudinal calculation and affects the model's emphasis on the trend of historical data. i Represents the current operation data, the latest data point used by the model at the current moment. i-1 Represents the data at the previous moment, the data point of the model before the current moment, which is used to compare with the current data point. Represents the regression value of the calculated data, which is the predicted or estimated value of the current data obtained through regression analysis. i Represents historical calculation data, the historical data points of the model before the current moment. i-1 Represents the data of a certain moment in history. The historical data points of the model before the current moment are used for comparison and calculation of data information. It represents the regression value of historical operation data, which can be obtained through regression analysis. i represents the operation order, n represents the number of operations, t represents the prediction period, k represents the ideal time data, δ represents the prediction operator, μ represents the cycle period, and di represents the integral sign;

[0114] This model combines a variety of forecasting techniques, including error squares, exponential decay, logarithmic transformation, regression analysis, nonlinear transformation, and integration. Through these combinations, the model attempts to capture the horizontal trends in the data, the dynamic changes in the time series, and the interactions between the data to predict energy use. It should be noted that the specific implementation and parameter selection of this model need to be adjusted according to the actual data characteristics and forecasting goals.

[0115] 4) Exponential smoothing;

[0116] Further smoothing calculation of the predicted data to ensure data availability;

[0117] P = αx i +(1-α)x i-1 +hx i (2)

[0118] In formula (2), P represents the smoothing calculation of the forecast index. In time series analysis, smoothing is usually used to reduce data noise. The smoothed data can show trends and periodicity more clearly.

[0119] α represents the time prediction parameter, which is a parameter between 0 and 1 and is used to control the degree of smoothing. The closer the α value is to 1, the stronger the smoothing effect is, that is, the more dependent on the most recent data points; the closer the α value is to 0, the weaker the smoothing effect is, that is, the less dependent on historical data. i Represents the operational data, which is the current data point in the time series, usually representing the observed value at a certain time step. h represents the prediction step size. This is the number of future time steps predicted from the current time point. For example, if the current time is day t, h=5 means predicting the value of day t+5. In time series analysis, smoothing is often used to reduce the noise of the data, and the smoothed data can show trends and periodicity more clearly. By adjusting α and h, the degree of smoothing of the prediction and the amount of advance prediction can be controlled to adapt to different prediction needs and data characteristics.

[0120] In a specific embodiment, the prediction algorithm is implemented based on the comparative analysis of real-time data and historical data, generating a trend line graph, calculating and analyzing the future growth rate and direction of the graphic trend, obtaining the prediction result, and continuously providing feedback to the prediction algorithm based on the actual operation effect, and modifying the algorithm parameters in real time to meet the dynamic changes of the system. In the application of the energy management system, the prediction algorithm needs to sort the data according to the time series, and compare and analyze the real-time data and historical data for prediction; the prediction algorithm calculates the current operation data x i With historical calculation data y i The squared difference is multiplied by the horizontal time prediction parameter α to obtain α(x i -y i ) 2 , by calculating the current operation data x i With the data x at the previous moment i-1 The ratio of the prediction period t is raised to the square of By calculating the current operation data x i The sum of the logarithms of get Adding the three formulas together we get This formula represents the horizontal calculation of data. By calculating the historical operation data y i The squared difference from the ideal time data k multiplied by the trend time prediction parameter β gives β(y i -k) 2 , by calculating the historical operation data y i With the data y at a certain moment in history i-1 The ratio of the prediction period t is raised to the square of By calculating the historical operation data y i The forecast period t is squared and then multiplied by the sum of the square roots. get Adding the three formulas together we get This formula represents the vertical calculation of data. By calculating the current operation data x i With historical calculation data y i The sum of the prediction period t is raised to the square of (x i +y i ) t , calculate the regression value of the operation data Regression value with historical calculation data

[0121] The convolution value of is multiplied by the prediction operator δ to obtain Calculate the current operation data x i With historical calculation data y i The difference divided by the historical calculation data y i The difference between the ideal time data k and the cycle period μ is then double-integrated to obtain Adding the three formulas together we get This formula represents the output value of the prediction result of time series data. The calculated prediction value needs to be further smoothed to eliminate the data with obvious errors. The smoothing calculation is performed by calculating the time prediction parameter α and the operation data x i The product of αx i , plus the difference between 1 and the time prediction parameter α multiplied by the calculation data x at the previous moment i-1 That is (1-α)x i-1 , plus the prediction step length h and the operation data x i The product of hx i , we get αx i +(1-α)x i-1 +hx i, which represents the smooth calculation of the prediction index to ensure the availability and practicality of the data. The prediction and smooth calculation of the data provide data support for the energy management of the system. The shorter the prediction period, the higher the accuracy of the predicted data, the higher the energy utilization rate, and the more cycles, the higher the energy utilization rate. It can be seen that the prediction period and the cycle period are in direct proportion to the prediction algorithm. The larger the value, the higher the accuracy. However, the value cannot be increased blindly. The maximum cycle period shall not exceed 100 times. Too many cycles will cause redundant calculation of data and cause data burden.

[0122] Furthermore, the implementation steps of the scheduling algorithm are:

[0123] 1) Determine priorities;

[0124] Determine the priority of energy scheduling from high to low, and adjust the priority in real time as the process progresses;

[0125] 2) Real-time scheduling;

[0126] Dispatch energy storage equipment based on real-time data to ensure stable operation of the system;

[0127]

[0128] In formula (3), F represents the real-time scheduling output value, a, b, c represent the priority of energy level, i represents the operation order, m represents the number of operations, A max Indicates the highest priority of the operation order, A all represents all energy levels, α represents the data heuristic value weight, and β represents the expected heuristic value weight;

[0129] 3) Predictive scheduling;

[0130] Based on the forecasted supply and demand results, energy storage equipment is scheduled in advance;

[0131]

[0132] In formula (4), G represents the predicted scheduling output value, X, Y, Q represent the priority of the predicted energy level, i represents the operation order, m represents the number of operations, and A max Indicates the highest priority of the operation order, A all represents all energy levels, α represents the data heuristic value weight, and β represents the expected heuristic value weight;

[0133] 4) Emergency dispatch;

[0134] Rapidly adjust the dispatch strategy in emergency situations to ensure the system's emergency response speed;

[0135]

[0136] In formula (5), F represents the real-time scheduling output value, which usually refers to the scheduling result or output value calculated based on real-time data and scheduling algorithms, such as emergency response, or the initiation of critical first aid, etc. a, b represent the priority of energy levels. a is a parameter used to represent the importance of different energy usage levels. It is usually used in multi-level energy management systems to determine which levels of energy usage should be given priority. b represents the priority of another energy level, which is used to compare and select the most appropriate energy usage level. ρ represents burst data, which is an irregular or abnormal data point that appears in time series data, such as due to emergencies or abnormal operations. i represents the order of operations, which is used to identify the order of data points or operation steps in time series analysis or scheduling algorithms. m represents the number of operations, the number of data points involved in a certain calculation or operation, or the number of tasks or operations that need to be processed. A max Indicates the highest priority in the order of operations. In priority scheduling, it refers to the identifier of the operation or task with the highest priority. all Represents all energy levels, α represents the data heuristic value weight. In priority scheduling, Amax refers to the identifier of the operation or task β with the highest priority. β represents the expected heuristic value weight. β is a weight coefficient used to indicate the importance of expected values ​​(such as goals, predictions, etc.) in the decision-making process. Q represents the burst state parameter, which is a parameter used to describe or quantify the impact of burst data on the system state.

[0137] In specific applications, the current highest priority energy usage level is determined according to the energy level priorities a and b, taking into account the impact of burst data ρ on the current state. The current operation sequence is determined according to the operation order i and the number of operations m. Using the historical data heuristic value weight α and the expected heuristic value weight β, combined with all energy levels in A_all, a comprehensive scheduling output value F is calculated. The final F will also be affected by the burst state parameter Q to adapt to the real-time changing environment.

[0138] In a specific embodiment, the scheduling algorithm allocates and uses the energy in the energy storage device based on the principle of efficient energy utilization to achieve the purpose of efficient utilization and energy saving. The scheduling algorithm first sorts the priority of the energy usage request and gives priority to requests with high priority; energy scheduling is divided into three types, namely: real-time scheduling, energy usage requests of the running system, predictive scheduling, based on the calculation of energy demand for a period of time in the future by the predictive algorithm, and storing and allocating energy in advance to prevent the phenomenon of insufficient energy during peak periods, and emergency scheduling, which quickly adjusts the request priority in the event of an emergency, runs the emergency as the highest priority, and allocates energy to other energy requests while ensuring the safe and stable operation of the system. The energy calculation formula for real-time scheduling calculates the high and low priority a of the energy level. i , b i , c i And select the highest priority and sum it up to get By calculating the highest priority A in the order of operations max With all energy levels A all The ratio is worth By calculating the first energy level a i The second energy level b is convolved by the power of the data heuristic value weight α i The expected heuristic value weight β is raised to the power of the first energy level a i With the second energy level b i The product of Adding the three formulas together we get This formula represents the output value of the real-time energy scheduling data; the energy calculation formula for the predicted scheduling is calculated by calculating the priority X of the predicted energy level i , Y i , Q i And select the highest priority and sum it up to get By calculating the highest priority A in the order of operations max With all energy levels A all The ratio is worth Predict the first energy level X by calculation i The second energy level Y is predicted by the square convolution of the data inspiration value weight α i The expected heuristic value weight β is divided by the predicted first energy level X i and predicted second energy level Y i The product of Adding the three formulas together we get This formula represents the dispatch data output value of the predicted energy; the energy calculation formula for emergency dispatch is calculated by calculating the high and low priority a of the energy level i , b iAnd the burst data priority ρ and select the highest priority and sum them to get By calculating the highest priority A in the order of operations max With all energy levels A all The ratio is worth By calculating the first energy level a i The second energy level b is convolved by the power of the data heuristic value weight α i The expected heuristic value weight β is raised to the power of the first energy level a i With the second energy level b i The product of By calculation 3 The product of the burst state parameter Q times the square of the burst data ρ divided by 2 Then sum it up to get Adding the four equations together we get This formula represents the output value of energy scheduling for burst data. The use of scheduling algorithms is much higher than the comparison value in real-time scheduling, predictive scheduling and emergency scheduling than the use of scheduling algorithms without scheduling algorithms, and the energy utilization rate is also higher. The scheduling algorithm can call all energy values ​​in emergency situations, and can also allocate as much energy value of the system as possible in predictive situations to ensure a stable supply of energy.

[0139] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. An energy management system for an energy storage system, characterized in that: It includes monitoring and acquisition module, communication module, control module, battery management module, energy management module, safety protection module and human-computer interaction module; among which: A monitoring and acquisition module monitors and acquires working status information of the energy storage device in real time through a sensor network, wherein the working status information includes at least current, voltage, temperature and humidity; A communication module, used to realize external information interaction between the power grid, renewable energy power generation system and load management system, the communication module includes a communication controller and a communication interface connected to the communication controller, a network protocol identification module, a network conversion device and an information sending and receiving prompt module; A control module receives data from the communication module based on a multi-level control system, wherein the multi-level control system includes a charge and discharge control system, an inverter control system, an energy storage control system, and an energy storage system coordination control system; the output end of the charge and discharge control system is connected to the input end of the inverter control system; the output end of the inverter control system is connected to the input end of the energy storage control system; the output end of the energy storage control system is connected to the input end of the energy storage system coordination control system; A battery management module is used to manage energy storage and energy storage status. The battery management module includes a battery status detection module, a charge and discharge balance control module, an overcurrent protection module, a charge and discharge status evaluation module and an energy storage status display module; the output end of the battery status detection module is connected to the input end of the charge and discharge balance control module; the output end of the charge and discharge balance control module is connected to the input end of the overcurrent protection module; the output end of the overcurrent protection module is connected to the input end of the charge and discharge status evaluation module; The energy management module includes an energy optimization unit, a supply and demand analysis unit, an energy conversion unit and an energy scheduling unit. The supply and demand analysis unit predicts energy demand through a prediction algorithm, and the energy scheduling unit comprehensively manages energy in the energy storage device through a scheduling algorithm; the output end of the energy optimization unit is connected to the input end of the energy conversion unit; the output end of the supply and demand analysis unit is connected to the input end of the energy conversion unit; the output end of the energy conversion unit is connected to the input end of the energy scheduling unit; A safety protection module, based on the data information of the monitoring and acquisition module and the rated threshold information, uses a circuit breaker and an overload protector to cut off the power supply when the energy storage device works abnormally, so as to ensure the safety of the energy storage device; The human-computer interaction module displays the status information of the energy storage system to the operator in real time through the display screen, and allows the operator to adjust the system operation through the control panel; The output end of the monitoring and acquisition module is connected to the input end of the communication module; the input end of the communication module is connected to the output end of the control module; the output end of the control module is connected to the input ends of the battery management module, the energy management module and the human-computer interaction module; the output end of the safety protection module is connected to the input ends of the monitoring and acquisition module, the battery management module and the energy management module; the output end of the human-computer interaction module is connected to the input ends of the control module and the energy management module.

2. The energy management system for an energy storage system according to claim 1, characterized in that: The sensor network includes a variable frequency power sensor, a temperature sensor and a humidity sensor; the variable frequency power sensor measures the fundamental and harmonic waves of the variable frequency electricity of the energy storage system and performs signal conversion and Fourier change calculation on it, and then directly outputs current and voltage values; the temperature sensor monitors the temperature changes of the energy storage device and the battery pack in real time based on thermistors; the humidity sensor monitors the changes in the content of water molecules in the energy storage device, the battery pack and the atmospheric environment based on nano gas sensors, and then monitors the changes in humidity; the data monitored by the sensor is transmitted to the control module in real time through the communication module and the data is saved.

3. The energy management system for an energy storage system according to claim 1, characterized in that: The network protocol identification module performs protocol analysis and identification on the communication protocol through a bidirectional linked list and a parsed data queue of the data group; the network conversion device converts energy data through a converter, and converts the analog signals of the data information of the power grid, renewable energy power generation system and load management system into digital signals; the information transceiver prompt module sends and receives text messages indicating successful signal transmission through the STM32F103C8 chip; the output end of the network protocol identification module is connected to the network conversion device and the input end of the information transceiver prompt module.

4. The energy management system for an energy storage system according to claim 1, characterized in that: The battery charge and discharge control system controls the charge and discharge current, charge and discharge voltage and charge and discharge cut-off amount of the energy storage device through a bidirectional converter, and implements a two-charge and two-discharge charge and discharge method to improve energy utilization; the inverter control system controls the inverter of the energy storage device through a voltage start loop and a three-phase closed-loop control loop; the energy storage control system monitors the maximum storage capacity and current remaining amount of energy of the battery of the energy storage device in real time, and transmits a signal to the battery charge and discharge control system to stop charging when the energy reaches the maximum value, and transmits a signal to the battery charge and discharge control system to charge immediately when the energy is lower than the minimum storage value and the energy drops sharply; the energy storage system coordination control system balances the production and consumption of energy through the proportional relationship between the consumed energy and the reserved energy, and analyzes the scheduling mode of the state of the energy storage device through the energy control method to control the input and output of energy.

5. The energy management system for an energy storage system according to claim 4, characterized in that: The implementation steps of the energy control method are: The system charge and discharge parameters and DC side battery system parameters are set according to the rated working data of the energy storage equipment, where the nominal capacity is 235KWh, the rated charge and discharge rate is 0.5P / 0.5P, the nominal voltage is 832V, the working voltage range is 728~949V, the rated power in the PSC parameters is 100KW*1, and the rated voltage on the AC side is 400V; 2) Start the charging and discharging process of the energy storage device by sending instructions to the battery management module, and monitor the battery status and feed it back to the main control unit of the control module; 3) Based on the working data and real-time energy demand fed back by the energy management module, the charging and discharging power of the energy storage device is adjusted to meet the needs of the power grid and users; 4) Based on the prediction of time and energy by the prediction algorithm, the energy demand is obtained, and the charging and discharging time of the system equipment is controlled according to the actual demand, charging during peak hours and discharging during off-peak hours to achieve efficient use of electric energy.

6. The energy management system for an energy storage system according to claim 1, characterized in that: The energy optimization unit optimizes the design and control of the energy system of the energy storage device based on the actual charging and discharging requirements and time of the system and the energy usage requirements of the user, and obtains the optimal energy usage plan through in-depth analysis of energy usage and energy storage; The supply and demand analysis unit analyzes the supply and demand balance of the system and predicts future energy supply and demand based on the energy data and historical energy data of the monitoring and acquisition module through a prediction algorithm; the energy conversion unit converts alternating current into direct current through a frequency converter, and converts thermal energy into electrical energy through a heat exchanger to achieve efficient use of energy; the energy scheduling unit uniformly schedules and manages the system energy based on the data provided by the supply and demand analysis unit, and uses a scheduling algorithm to reasonably allocate and efficiently utilize energy.

7. An energy management method for an energy storage system, characterized in that: An energy storage system energy management system according to any one of claims 1 to 6 is applied, and the energy storage system energy management method comprises the following steps: Step 1: Collect the operating data of each device in the energy storage system in real time through sensors and communication interfaces, including at least voltage, current, temperature, state of charge and health status; Step 2: Based on the collected data information and historical data, the energy supply and demand of the energy storage system is analyzed through the prediction algorithm; Step 3: Optimize the energy usage of the energy storage system based on the analysis and prediction results, and determine the charging and discharging planning curve; Step 4: Based on the optimization results, the charging and discharging power of the battery pack is controlled through the scheduling algorithm and the energy usage is allocated; Step 5: Monitor the operating status of the energy storage system and promptly notify the operator when the system collected data exceeds the threshold or a fault occurs; Step 6: Generate tables and curves of the operating status of the energy storage system and energy management system, and timely analyze and feedback the system adjustment parameters to ensure efficient operation of the system.

8. The energy management method of an energy storage system according to claim 7, characterized in that: The implementation steps of the prediction algorithm are: 1) Data collection and comparison; Collect system real-time monitoring data and system historical operation data, and compare the two sets of data to find similarities and differences; 2) Supply and demand analysis; Based on the analyzed data and the similarities between the two sets of data, the supply and demand requirements of the system energy are summarized; 3) Time series forecasting; The data is combined according to the time series, and the time data is used to predict the energy usage. The expression of the prediction algorithm is: 。 9. The energy management method of an energy storage system according to claim 7, characterized in that: The implementation steps of the scheduling algorithm are: 1) Determine priorities; Determine the priority of energy scheduling from high to low, and adjust the priority in real time as the process progresses; 2) Real-time scheduling; Dispatch energy storage equipment based on real-time data to ensure stable operation of the system;

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