The three-level linkage control strategy between the battery management system (BMS), energy storage converter (PCS), and energy management system (EMS) in the energy storage system is the key to ensuring efficient and safe operation of the system.
1. Linkage between BMS and PCS: Charge and discharge management
Example
In a typical energy storage application scenario, suppose we have an energy storage unit composed of multiple lithium batteries, each connected to a BMU (slave control unit), which in turn is connected to a BCU (master control unit), which in turn is connected to a BAU (main control unit).
When the energy storage system receives grid dispatch instructions, BAU will determine whether to allow charging or discharging based on the current SOC (battery remaining capacity) status of the system, and send the command to PCS.
If PCS detects excessive energy input on the grid side, it will activate charging mode; On the contrary, during peak electricity demand periods, PCS switches to discharge mode to support the grid.
Parameter
Maximum charging power: set to 200kW to ensure that the battery will not be damaged due to overcharging.
Maximum discharge power: set to 300kW to meet the demand for fast response during peak hours.
SOC upper and lower limits: usually maintained between 20% -80% to avoid the impact of deep charging and discharging on battery life.
2. Linkage between BMS and PCS: Temperature Management
Example
Considering that temperature has a significant impact on the performance of lithium batteries, BMS not only needs to monitor the voltage, current, and other information of individual batteries, but also needs to monitor their surface temperature.
Once the temperature of a certain battery module is found to be too high, the BMS triggers the PCS to limit the charging and discharging rate of that part, and even suspends operation until the temperature returns to normal. In addition, the temperature can be actively reduced by activating the cooling system.
Parameter
High temperature threshold: For example, 50 ° C, measures should be taken to protect the battery when this temperature is exceeded.
Low temperature threshold: such as 0 ° C, to prevent low temperature from affecting chemical reaction efficiency.
Temperature difference alarm value: The maximum allowable temperature difference between adjacent batteries is set to 5 ° C, and a warning will be issued if exceeded.
3. Collaborative work of BMS, PCS, and EMS: optimizing scheduling
Example
EMS is responsible for overall energy management and scheduling decisions, and can develop optimal charging and discharging plans based on real-time electricity prices, weather forecasts, and other factors.
For example, arranging PCS for charging during the nighttime valley price period and releasing stored energy during the daytime peak price period to earn the price difference. At the same time, EMS will continuously evaluate the health status of the entire system (including SOH) and adjust strategies accordingly to extend the service life of equipment.
Parameter
Peak shaving and valley filling strategy: Users can configure their own electricity price templates based on local time of use electricity prices, set the charging and discharging power of PCS during different time periods, and form a peak shaving and valley filling strategy template; Provide the function of configuring policy templates on a daily and weekly basis.
Demand control: EMS can predict future load demand and plan PCS actions in advance to ensure that the maximum demand specified in the contract is not exceeded, thereby avoiding additional costs.
Plan curve: For specific application scenarios (such as industrial users), EMS generates detailed daily or weekly operation guidelines to guide PCS in executing tasks according to the established schedule.
4. Security protection mechanism of BMS and PCS
Example
In order to further enhance the security of the system, a multi-layer protection mechanism has been established between BMS and PCS. For example, when BMS detects any abnormal situation (such as short circuit, overvoltage/undervoltage), it will immediately notify PCS to stop the relevant operation and may trigger an emergency disconnect device to cut off the power supply. In addition, there are hardware level protective measures such as fuses and relays to cope with fault isolation in extreme situations.
Parameter
Overcurrent protection: set to 1.5 times the rated current to prevent damage caused by excessive current.
Overvoltage/undervoltage protection: Set upper and lower limits respectively to ensure that the battery is always within a safe operating range.
Short circuit protection: In the event of a short circuit, quickly cut off the circuit to ensure the safety of personnel and property.
The integration of BMS (Battery Management System) and PCS (Energy Storage Converter) with other intelligent devices is one of the key steps in building an intelligent and efficient energy storage system. This integration is not limited to hardware level connections, but more importantly, it enables information sharing and collaborative work at the software level to optimize the entire energy management process.
Common integration methods and their characteristics:
1. Deep integration with EMS (Energy Management System)
Data exchange: BMS is responsible for collecting various operating parameters of the battery, such as voltage, current, temperature, SOC (remaining charge), SOH (health status), etc., and transmitting this information to EMS. At the same time, EMS will also send instructions to BMS after making decisions based on factors such as power grid conditions and user needs.
Strategy formulation: Based on data from BMS, EMS can more accurately predict the trend of battery state changes, thereby better planning charge and discharge plans. For example, arranging charging when electricity prices are low and releasing stored energy during peak hours to earn a price difference. In addition, EMS optimizes long-term energy scheduling strategies by analyzing historical data to ensure maximum economic benefits of the system.
2. Integration of smart home and building automation systems
Bidirectional communication: Modern smart home platforms typically support multiple protocols, allowing BMS/PCS to easily integrate into them. In this way, users can remotely monitor the operation of the energy storage system through mobile apps or other terminal devices, and adjust settings according to personal preferences. For example, setting the maximum power output within a specific time period, or choosing to prioritize the use of self generated electricity over mains power supply.
Linkage control: In addition to simple monitoring functions, smart home systems can also achieve linkage control with BMS/PCS. For example, when no one is detected at home, it automatically enters energy-saving mode to reduce unnecessary power consumption; Before family members return home, turn on high-power appliances such as air conditioning to ensure a comfortable living environment.
3. Role in microgrids
Multi source coordination: In a typical microgrid environment, in addition to energy storage devices, there are also various distributed power sources such as solar panels and wind turbines. At this point, BMS/PCS not only needs to consider its own working status, but also need to effectively coordinate and cooperate with other power sources to jointly maintain the supply-demand balance within the microgrid. For example, when there is an excess of electricity generated by photovoltaic arrays, PCS chooses to store the excess energy instead of directly feeding it back to the main grid.
Island operation capability: For microgrids with island operation capability, the role of BMS/PCS is particularly prominent. Once disconnected from the external power grid, they must quickly take over load distribution tasks to ensure the continuous power supply of important facilities is not affected. This requires BMS/PCS to have high stability and reliability, and be able to switch from grid connected to off grid mode in a short period of time.
4. Support for cloud platforms and big data analysis
Cloud computing: With the development of cloud computing technology, more and more enterprises are using cloud platforms for large-scale data processing and model training. For energy storage systems, this means uploading locally collected data to cloud servers, utilizing powerful computing resources to mine and analyze massive amounts of information, and obtaining more refined operational recommendations.
AI driven optimization: Utilizing artificial intelligence algorithms, especially machine learning methods, to identify potential patterns from a large amount of historical records and provide guidance for future operations. For example, predicting weather conditions in the next few days and making corresponding preparations in advance; Or automatically adjust the charging and discharging strategy based on the user's electricity usage habits, improving the user experience while also reducing costs.