Energy storage collaborative optimization linkage control method and system

Abstract

The application discloses a kind of energy storage collaborative optimization linkage control method and system, real-time acquisition battery internal state data and external demand and plan data;Based on the battery internal state data dynamic calculation optimal operation power interval of energy storage system;According to the external demand data and optimal operation power interval, collaborative optimization decision is carried out on multiple time scales, and final power instruction is generated;The final power instruction is issued to PCS execution, and in the execution process, according to the microscopic state change data of battery, the final power instruction is dynamically adjusted.The application can realize the efficient cooperation between battery management system, energy management system and power conversion system, optimize the operation of energy storage system, thereby reduce the life cycle cost, improve operation safety, enhance the response capability to grid instruction.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system control technology, specifically relating to an energy storage collaborative optimization linkage control method and system. Background Technology

[0002] In the actual operation of energy storage systems, there is a significant disconnect in data interaction and decision-making coordination among the Battery Management System (BMS), Energy Management System (EMS), and Power Conversion System (PCS). The BMS primarily focuses on monitoring parameters such as cell voltage, temperature, and state of charge (SOC) to achieve overcharge and over-discharge protection and balancing management. However, its control logic is limited to the safety of the battery itself and fails to establish an effective connection with the dynamic demands of the grid. The EMS generates charge and discharge plans based on grid dispatch instructions, electricity price signals, and load forecast data. However, these plans are typically based on macro-level economic or dispatch objectives, ignoring the micro-level evolution of the battery pack, such as key factors like SOC imbalance between cells, health degradation, and internal resistance differences. The PCS, as an execution unit, mechanically responds to power commands from the Energy Management System, lacking the ability to perceive the real-time state of the battery and a dynamic adjustment mechanism.

[0003] This control disconnect leads to multiple technical obstacles: at the economic level, the EMS's scheduling strategy may maintain high-intensity charging and discharging operations even when the battery's health deteriorates, leading to accelerated battery life degradation and thus increasing the system's total life cycle cost; at the safety level, macro-level power commands are unable to identify the overload risk of weak cells within the battery pack, easily inducing local heat accumulation or even thermal runaway; at the grid support level, in the face of rapidly changing grid commands such as frequency regulation and peak shaving, the system cannot accurately match power and respond instantaneously based on the actual available battery capacity, weakening the energy storage system's support efficiency for grid stability.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the aforementioned background technology and provide a method, system, electronic device, and computer-readable storage medium for coordinated optimization and linkage control of energy storage. This enables efficient coordination between the BMS, EMS, and PCS, optimizes the operation of the energy storage system, thereby reducing the total lifecycle cost, improving operational safety, and enhancing responsiveness to grid commands.

[0006] The technical solution adopted in this invention is: a collaborative optimization and linkage control method for energy storage, comprising the following steps: Real-time acquisition of battery internal status data from BMS and external demand and planning data from EMS; The optimal operating power range of the energy storage system is dynamically calculated based on the internal state data of the battery. Based on the external demand data and the optimal operating power range, collaborative optimization decisions are made at multiple time scales to generate the final power command. The final power command is sent to the PCS for execution. During the execution process, the final power command is dynamically adjusted based on the micro-state change data of the battery fed back in real time by the BMS.

[0007] An energy storage collaborative optimization and linkage control system includes a data acquisition module, a data processing module, a multi-timescale decision-making module, and an adaptive control module integrated into the same collaborative control platform. The data acquisition module is used to acquire battery internal status data from BMS and external demand and planning data from EMS in real time. The data processing module is used to dynamically calculate the optimal operating power range of the energy storage system based on the internal state data of the battery. The multi-timescale decision module is used to make collaborative optimization decisions on multiple timescales based on the external demand data and the optimal operating power range, and generate the final power command. The adaptive control module is used to send the final power command to the PCS for execution. During the execution process, the final power command is dynamically adjusted based on the micro-state change data of the battery fed back by the BMS in real time.

[0008] An electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: when the processor executes the computer program, the electronic device implements the energy storage collaborative optimization linkage control method as described in any of the preceding claims.

[0009] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the energy storage collaborative optimization and linkage control method as described in any of the preceding claims.

[0010] The beneficial effects of this invention are as follows: This invention acquires battery internal state data from the Battery Management System (BMS) and external demand and planning data from the Energy Management System (EMS) in real time. Based on the battery internal state data, it dynamically calculates the optimal operating power range of the energy storage system. Based on the external demand data and the optimal operating power range, it performs collaborative optimization decisions at multiple time scales to generate the final power command. The final power command is then sent to the Power Conversion System (PCS) for execution, and dynamic adjustments are made based on real-time feedback from the BMS during execution. By integrating the battery's internal microstate with external macro demand, this invention solves the problems of economic degradation, safety hazards, and insufficient grid support caused by control disconnect in existing technologies. It enables efficient collaboration between the battery management system, energy management system, and power conversion system, optimizes the operation of the energy storage system, thereby reducing the total life cycle cost, improving operational safety, and enhancing the responsiveness to grid commands. Attached Figure Description

[0011] Figure 1 This is a flowchart of the energy storage collaborative optimization linkage control method of the present invention.

[0012] Figure 2 This is a flowchart for calculating the optimal operating power range according to the present invention.

[0013] Figure 3 This is a flowchart of the multi-timescale optimization decision-making process of the present invention.

[0014] Figure 4 This is a schematic diagram of the energy storage collaborative optimization and linkage control system of the present invention. Detailed Implementation

[0015] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments to facilitate a clear understanding of the present invention, but these descriptions do not constitute a limitation on the present invention.

[0016] It should be understood that, when used in this application specification and the appended claims, the term includes indicating the presence of the described feature, integral, step, operation, element and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0017] Furthermore, in the description of this application and the appended claims, the terms first, second, third, etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. References to one or more embodiments described in this application mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, statements appearing in different parts of this specification—in one embodiment, some embodiments, some other embodiments, some still other embodiments, etc.—do not necessarily refer to the same embodiment, but rather mean one or more, but not all, embodiments, unless otherwise specifically emphasized.

[0018] like Figure 1-3 As shown, this application proposes a collaborative optimization and linkage control method for energy storage, including the following steps: Real-time acquisition of battery internal status data from BMS and external demand and planning data from EMS; The optimal operating power range of the energy storage system is dynamically calculated based on the internal state data of the battery. Based on the external demand data and the optimal operating power range, collaborative optimization decisions are made at multiple time scales to generate the final power command. The final power command is sent to the PCS for execution. During the execution process, the final power command is dynamically adjusted based on the micro-state change data of the battery fed back in real time by the BMS.

[0019] For ease of understanding, the following explains some key terms in this embodiment: BMS, or Battery Management System, is primarily responsible for monitoring and managing parameters such as voltage, current, and temperature of the battery pack to ensure its safe operation.

[0020] EMS, or Energy Management System, is mainly responsible for the macro-level scheduling and optimization of the entire energy system, such as formulating charging and discharging plans based on factors like grid demand and electricity prices.

[0021] An energy storage system is a system that can store electrical energy and release it when needed. It typically consists of battery packs, BMS, PCS, etc.

[0022] The optimal operating power range refers to the maximum range of charge and discharge power allowed by an energy storage system under safe operating conditions and specific performance requirements.

[0023] Collaborative optimization decision-making refers to the decision-making process that coordinates the operation of different systems or modules to achieve overall optimality, taking into account multiple objectives and constraints.

[0024] The final power command refers to the actual charge / discharge power command issued to the PCS for execution after collaborative optimization decision-making.

[0025] PCS, or power conversion system, is mainly responsible for converting the direct current (DC) from the battery into alternating current (AC) and supplying it to the power grid, or converting the AC power from the power grid into DC to charge the battery.

[0026] Microstate change data refers to the subtle state changes that occur inside a battery within a short period of time, such as instantaneous voltage fluctuations or rapid temperature increases.

[0027] Dynamic adjustment refers to the immediate modification and correction of predetermined instructions or parameters based on real-time feedback data during system operation.

[0028] In one implementation, battery internal status data is first acquired in real time from the BMS, and external demand and planning data are acquired from the EMS. Specifically, battery internal status data can be periodically collected at a preset fixed frequency via the BMS's communication interface, for example, every few minutes. External demand and planning data can be sent by the EMS to the collaborative control platform via a standard communication protocol.

[0029] Secondly, the optimal operating power range of the energy storage system is dynamically calculated based on the acquired battery internal state data. For example, the allowable healthy current for controlling degradation can be determined by using a preset lookup table or lifetime model based on the battery's current state of charge (SOC) and state of health (SOH), and then the corresponding charging and discharging power can be determined accordingly.

[0030] Furthermore, collaborative optimization decisions are made based on external demand data and the optimal operating power range to generate the final power command. Specifically, a preliminary power demand curve can first be generated based on external demand data, such as grid dispatch commands; this part can be led by the EMS (Electrical Management System). Subsequently, the collaborative control platform leads the comparison of this preliminary power demand curve with the optimal operating power range. If the preliminary power demand curve exceeds the optimal operating power range, it is restricted to within the range.

[0031] Finally, the final power command is sent to the PCS for execution. During execution, the final power command is dynamically adjusted based on real-time feedback data on the microstate changes of the battery from the BMS. For example, if a rapid drop in the voltage of a single cell within the battery pack is detected, an early warning mechanism can be triggered, and the currently issued discharge power command can be slightly reduced.

[0032] The energy storage collaborative optimization and linkage control method proposed in this application effectively solves the problem of control disconnect in traditional energy storage systems by establishing a deep data interaction and collaborative decision-making mechanism among the BMS, EMS, and PCS. This method can balance the internal safety of the battery with the external grid demand, dynamically adjust the operating strategy, thereby avoiding the risk of battery overload or damage, and improving the system's economy, safety, and accurate and rapid response capability to grid frequency regulation and peak shaving commands.

[0033] In one embodiment, this application further proposes that the internal state data of the battery includes at least one of the following: cell voltage, cell temperature, SOC (state of charge), SOH (state of health), imbalance, and internal resistance; and the external demand data includes at least one of the following: grid dispatch instructions, electricity price, and load forecast.

[0034] Battery internal status data refers to the real-time operating parameters and health status indicators within the battery cells or battery packs of an energy storage system. Its purpose is to provide fundamental data support for the safe operation, lifespan management, and performance optimization of the energy storage system. This data can be collected and calculated in real time through sensors and algorithms built into the Battery Management System (BMS), or periodically monitored and uploaded to the BMS by external diagnostic equipment.

[0035] External demand data refers to the requirements and reference information provided by the external environment on the operation of the energy storage system. Its role is to guide the energy storage system in economic optimization, responding to grid dispatch, and meeting load demand. This data can be obtained through the Energy Management System (EMS) from grid dispatch centers, electricity trading markets, meteorological departments, etc., or directly through local sensors or interfaces. Specifically, grid dispatch instructions refer to the specific requirements issued by the grid operation department to the energy storage system regarding charging and discharging power, time, response speed, etc., ensuring that the energy storage system can cooperate with the grid in providing ancillary services such as peak shaving, frequency regulation, and backup. Electricity price refers to the price of electricity at different times in the electricity market, providing a basis for economic optimization of the energy storage system. Load forecasting refers to the prediction of electricity load demand over a future period, helping the energy storage system plan its charging and discharging strategies in advance.

[0036] This application explicitly defines the specific content of the battery's internal state data and external demand data. Specifically, the battery's internal state data includes at least one of the following: cell voltage, cell temperature, state of charge (SOC), state of equilibrium (SOH), imbalance, and internal resistance. These parameters comprehensively cover the battery's key operating states and health indicators from microscopic to macroscopic levels. For example, real-time monitoring of cell voltage and temperature directly reflects the battery's safety boundaries and thermal runaway risk; SOC and SOH provide information on the battery's available energy and lifespan degradation; and imbalance and internal resistance reveal the battery pack's internal consistency and power output capability. This refined data enables the energy storage system to more accurately assess the battery's actual capacity and health status when dynamically calculating the optimal operating power range, thereby setting more reliable safety boundaries. Simultaneously, external demand data includes at least one of the following: grid dispatch instructions, electricity prices, and load forecasting. This data provides comprehensive external environmental information for the operation of the energy storage system. Grid dispatch instructions ensure that the energy storage system can accurately respond to the grid's ancillary service needs such as peak shaving and frequency regulation, enhancing the grid's support capabilities. Electricity price information provides the system with a basis for economic optimization, enabling it to maximize revenue through intelligent charging and discharging strategies. Load forecasting helps the system plan ahead and better meet future load demands. Given the aforementioned specific and comprehensive internal and external data, the collaborative optimization and linkage control method of this application enables deeper data interaction and collaborative decision-making among the BMS, PCS, and EMS. During the generation of the final power command and its issuance to the PCS for execution, the system can comprehensively consider the battery's microstate and external demands, formulating an optimization strategy that ensures battery safety and lifespan while meeting grid dispatch and economic objectives. This not only solves the problems of insufficient data interaction and control disconnect in existing technologies but also improves the overall operational safety, economy, and grid support capabilities of the energy storage system, achieving refined and intelligent management of the energy storage system.

[0037] In one embodiment, this application further proposes a method for dynamically calculating the optimal operating power range of an energy storage system based on the battery's internal state data, which includes: calculating five boundary constraints based on the battery's internal state data; determining the limiting current corresponding to each boundary constraint based on the five boundary constraints; fusing the limiting currents and taking the intersection to determine the final safety boundary; and determining the optimal operating power range based on the safety boundary.

[0038] Specifically, calculating the five boundary constraints involves identifying and quantifying key operational limitations affecting battery safety and lifespan based on real-time internal state data of the battery. These constraints aim to comprehensively assess the battery's health status and operational risks from multiple dimensions.

[0039] Based on this, determining the limiting current corresponding to each boundary constraint means transforming the aforementioned abstract boundary constraints into specific, operable current limits. This step maps the battery's physical and chemical limitations onto electrical parameters to facilitate subsequent power control.

[0040] Furthermore, determining the final safety boundary by merging and intersecting the limiting currents means comprehensively considering the limiting currents determined by all boundary constraints and determining a unified and safe charging and discharging current range using the most conservative principle. This process ensures that the energy storage system will not exceed its limits under any single constraint condition. Specifically, for charging operations, the maximum allowable charging currents determined by all boundary constraints are compared, and the minimum value is taken as the final maximum allowable charging current; for discharging operations, the maximum allowable discharging currents determined by all boundary constraints are similarly compared, and the minimum value is taken as the final maximum allowable discharging current. This intersection-based approach effectively avoids potential safety risks caused by ignoring a single constraint.

[0041] Finally, determining the optimal operating power range based on the aforementioned safety boundaries involves converting the finalized safe current range into a power range usable by the energy storage system. This is a crucial step in aligning the underlying battery safety management with the scheduling requirements of the upper-level system. For example, the maximum allowable charging power and maximum allowable discharging power can be directly calculated by multiplying the finalized maximum allowable charging current and maximum allowable discharging current by the current voltage (or average voltage) of the battery pack, respectively.

[0042] Through the above technical solution, this application can comprehensively and accurately assess the operational safety boundary of an energy storage system. By calculating five boundary constraints, it ensures that multiple key factors such as battery voltage, temperature, hardware, lifespan degradation, and consistency are taken into consideration, avoiding safety blind spots that may arise from single-dimensional assessments. These constraints are transformed into specific limiting currents, making the abstract safety concept concrete and actionable control parameters. Furthermore, by fusing and intersecting all limiting currents, this application can determine the final safety boundary using the most conservative and stringent principles, effectively mitigating overall system risks caused by local weaknesses and improving the operational safety of the energy storage system. Finally, based on this precise safety boundary, the optimal operating power range is determined, providing a reliable power range for upper-level scheduling. This ensures that the battery operates within a safe range while maximizing its usable capacity and lifespan, thereby improving the economy and reliability of the energy storage system while ensuring safety.

[0043] In one embodiment, this application further proposes that the five boundary constraints are voltage constraint, temperature constraint, hardware constraint, lifetime decay constraint, and consistency safety constraint; the limiting currents corresponding to each boundary constraint are the instantaneous charge and discharge current determined based on the limiting voltage, the current corresponding to the allowable heat generation power determined based on the limiting temperature, the maximum hardware current determined based on the PCS and connector ratings, the healthy current allowed by the control decay rate determined based on SOH and SOC, and the current allowed to prevent inconsistency deterioration determined based on SOC imbalance.

[0044] Voltage constraints refer to limitations set to ensure that the voltage of individual battery cells or battery packs remains within a safe operating range, preventing irreversible damage from overcharging or over-discharging. This can be achieved by real-time monitoring of individual battery cell voltages and comparing them to preset maximum and minimum voltage thresholds; exceeding these thresholds triggers the voltage constraint. Temperature constraints, on the other hand, are temperature range limits set to prevent batteries from operating at excessively high or low temperatures, which could lead to performance degradation, shortened lifespan, or even thermal runaway. This can be achieved by deploying temperature sensors on the surface of battery modules or individual cells to acquire temperature data in real time and compare it to safe upper and lower temperature limits.

[0045] Hardware constraints refer to the maximum current or power limits that the power conversion system (PCS) and connectors in an energy storage system can withstand, ensuring safe and stable operation. This can be achieved by setting the maximum allowable current for the entire system based on the rated current and power of the PCS, as well as the current-carrying capacity of components such as connecting cables and busbars. Lifetime degradation constraints are operating parameter limits set to slow down battery capacity degradation and extend battery life, typically related to the battery's state of health (SOH) and state of charge (SOC). This can be achieved by determining the maximum allowable charge / discharge current under the current state of health based on the battery's SOH and SOC using lookup tables or empirical formulas, thus controlling the degradation rate. Consistency safety constraints are limits set to prevent localized overcharging and over-discharging of individual cells within a battery pack due to SOC imbalance, which could lead to safety hazards or accelerate overall degradation. This can be achieved by real-time monitoring of the SOC differences among individual cells within the battery pack; when the imbalance exceeds a preset threshold, the overall charge / discharge current is limited to allow time for the balancing system to operate.

[0046] Furthermore, the instantaneous charge and discharge current determined based on the limiting voltage serves to calculate, by measuring the individual cell voltage in real time and combining it with the battery's internal resistance model, the maximum instantaneous charging current and maximum instantaneous discharging current that the battery can withstand at the current moment without exceeding the preset upper or lower voltage limits.

[0047] The current corresponding to the allowable heat generation power determined by the extreme temperature is used to establish a battery thermal model. Based on the battery's heat generation characteristics, combined with the battery's heat dissipation capacity and the preset maximum allowable temperature, the maximum allowable heat that the battery can generate under the current environmental conditions is calculated, and then the corresponding maximum charge and discharge current is deduced.

[0048] The maximum current of the hardware, determined based on the PCS and connector ratings, serves to set the maximum allowable current value at the system level, based on the PCS nameplate parameters and the current-carrying capacity of electrical connection components such as connecting cables, busbars, and circuit breakers, in order to protect the hardware from damage due to overload.

[0049] The healthy current allowed by the controlled degradation rate determined based on SOH and SOC is used to calculate the maximum charge and discharge current that the battery can withstand while ensuring that the battery life degradation rate is within an acceptable range, by combining the battery's SOH and SOC data and using a preset degradation model or empirical curve.

[0050] The current allowed to prevent the inconsistency from worsening is determined based on the SOC imbalance. Its function is to calculate the SOC difference of each cell in the battery pack in real time. When the imbalance reaches a certain level, the system will actively reduce or limit the charging and discharging current to buy time for the equalization function of the battery management system (BMS) and prevent the imbalance from expanding further.

[0051] Through the aforementioned technical solution, this application ensures comprehensive coverage of key safety factors in battery operation when dynamically calculating the optimal operating power range by specifically defining five boundary constraints and their corresponding limiting currents. Clearly defined voltage constraints, temperature constraints, hardware constraints, lifespan degradation constraints, and consistency safety constraints cover core aspects such as battery voltage safety, temperature control, hardware safety, lifespan optimization, and individual cell consistency, avoiding risk omissions due to missing constraint types. For each boundary constraint, a corresponding limiting current calculation method is specified. These specific definitions make the boundary constraint calculation more targeted and complete, improving system safety and decision-making accuracy, thereby making the energy storage system safer and more reliable in operation and effectively extending battery life.

[0052] In one embodiment, this application further proposes that the final safety boundary includes the maximum allowable charging current and the maximum allowable discharging current determined by taking the minimum value of each boundary and then taking the intersection of the values. The maximum allowable charging power and the maximum allowable discharging power are determined based on the maximum allowable charging current and the maximum allowable discharging current as the optimal operating power range.

[0053] Specifically, the conservative approach of taking the minimum value of each boundary condition and then finding the intersection means that when calculating the safe operating boundary of an energy storage system, the system comprehensively considers the limiting currents corresponding to all determined boundary constraints (such as voltage constraints, temperature constraints, hardware constraints, lifetime degradation constraints, consistency safety constraints, etc.). To ensure the highest level of safety, the system selects the smallest value among these limiting currents as the final limit. For example, if the voltage constraint allows 100A, the temperature constraint allows 90A, and the hardware constraint allows 120A, then under the conservative principle, 90A will be taken as the current maximum allowable current. This method ensures that the energy storage system always operates within the most stringent range of all constraints, thereby effectively avoiding safety risks caused by the breach of a single constraint.

[0054] The maximum permissible charging current and maximum permissible discharging current are calculated in real-time, based on the aforementioned conservative principles and taking into account all dynamically changing internal battery state data, representing the highest charging and discharging currents the battery system can withstand at the current moment. Determining the maximum permissible charging power and maximum permissible discharging power based on the maximum permissible charging current and maximum permissible discharging current involves converting the aforementioned safe current limits into power limits so that the power conversion system (PCS) can directly understand and execute them. Typically, the power can be determined by multiplying the maximum permissible charging current or maximum permissible discharging current by the current real-time voltage (or average voltage) of the battery pack.

[0055] Ultimately, these determined maximum allowable charging power and maximum allowable discharging power together constitute the optimal operating power range. This range defines the power range within which the energy storage system can operate safely and stably at the current moment. This range is dynamically updated based on real-time changes in the battery's internal state and transmitted in real-time to the multi-timescale decision-making module as a constraint for its collaborative optimization decisions.

[0056] Through the above technical solutions, this application ensures that the energy storage system operates within a strictly safe range under any operating conditions. By adopting a conservative principle of minimizing each boundary value and then taking the intersection, safety risks such as overcharging, over-discharging, or localized thermal runaway caused by the breach of a single constraint are effectively avoided, thus improving the operational safety of the energy storage system. Converting current limits into power limits allows the power conversion system to directly receive and execute safety commands, simplifying the control logic and ensuring the safety of power commands. Simultaneously, using this as the optimal operating power range provides a precise and dynamically updated safe operating range for subsequent collaborative optimization decisions, enabling the system to strictly guarantee battery safety and lifespan while pursuing economic benefits.

[0057] In one embodiment, this application further proposes an energy storage collaborative optimization and linkage control method, which performs collaborative optimization decisions on multiple time scales based on external demand data and the optimal operating power range to generate a final power command. The method specifically includes: At the first time scale, based on electricity prices and load forecasts, a planned charge and discharge power curve is generated with the goal of optimal economic efficiency. On the second time scale, the planned charge and discharge power curve is compared and corrected with the optimal operating power range. If the planned charge and discharge power curve exceeds the optimal operating power range, the planned charge and discharge power curve is limited to the optimal operating power range, and a safe power command is generated; otherwise, a safe power command is generated directly based on the planned charge and discharge power curve. In the third time scale, the secure power command is used as the final power command; The first time scale is at the hour or day level, the second time scale is at the minute or second level, and the third time scale is at the millisecond or second level.

[0058] Specifically, this collaborative optimization decision aims to comprehensively consider external economic demands and the internal safety status of the battery, and through a layered and time-segmented strategy, generate power commands that can both meet grid dispatch or market demands and ensure the safe and stable operation of the battery system.

[0059] The first time scale typically corresponds to a longer planning period, such as hours or days. At this time scale, the system primarily focuses on the economic benefits of the energy storage system. Electricity price data reflects price signals from the electricity market, while load forecasting provides trends in future electricity demand. Based on this data, the system uses optimization algorithms, such as linear programming, dynamic programming, or model predictive control (MPC), to calculate the charge / discharge power plan that achieves maximum revenue or minimum cost throughout the planning period. This planned charge / discharge power curve reflects the operating strategy of the energy storage system under ideal economic conditions, such as charging during off-peak hours and discharging during peak hours, or peak shaving and valley filling based on load forecasting.

[0060] The second timescale, typically at the minute or second level, primarily serves to perform real-time or near-real-time safety verification of the macroeconomic plan generated at the first timescale. The optimal operating power range is dynamically calculated based on the battery's internal state data, representing the maximum charge and discharge power range the battery can safely withstand at the current moment. The system compares the planned charge and discharge power curve generated at the first timescale with this optimal operating power range point by point. If the planned power exceeds the optimal operating power range (e.g., the planned charging power exceeds the maximum allowable charging power, or the planned discharging power exceeds the maximum allowable discharging power), the system will perform a limiting operation, adjusting the excess power value to the boundary value of the optimal operating power range, thereby generating a safe power command that considers both economic efficiency and battery safety operating conditions. If the planned power is entirely within the optimal operating power range, no limiting is required, and the planned power is directly used as the safe power command.

[0061] The third timescale, typically in the millisecond or second range, represents the actual timescale for executing control commands. At this timescale, the safety-verified power command, after safety correction based on the second timescale, is directly adopted and issued to the PCS for execution as the final power command. This means that within an extremely short time, the system transmits the safety-verified power command to the execution layer to achieve precise control of the energy storage system. This rapid response capability is crucial for meeting the real-time dispatch needs of the power grid, such as frequency regulation and voltage regulation.

[0062] The different time scales are set to accommodate the varying needs and response speeds at different levels of energy storage system operation. Hourly or daily time scales are suitable for macro-level economic dispatch and long-term planning. Minute or second-level time scales are suitable for real-time or near-real-time safety corrections to macro-level plans to address rapid changes in battery status or temporary adjustments to grid commands. Millisecond or second-level time scales are suitable for actual power command execution and rapid response, ensuring that the energy storage system can respond to the instantaneous demands of the grid in a timely and accurate manner.

[0063] Through the above technical solution, this application fully utilizes external demand data for economic optimization at the first time scale, avoiding the impact of short-term fluctuations on long-term cost control; at the second time scale, it ensures that the plan remains within a safe range through limiting processing, effectively preventing battery overload risks; and at the third time scale, it achieves rapid execution response to grid demand. This multi-time scale collaborative optimization decision-making mechanism dynamically integrates economic objectives and safety constraints, ensuring that the final power command achieves a balance between macro-planning and micro-safety, thereby maximizing the economic benefits and grid support capabilities of the energy storage system while ensuring the safe operation of the battery system and extending its service life.

[0064] In one embodiment, this application further proposes to dynamically adjust the final power command based on the micro-state change data of the battery fed back in real time by the BMS, specifically including: real-time monitoring of the battery voltage change rate and / or temperature rise rate; when the voltage change rate and / or temperature rise rate exceeds a preset safety threshold, generating an emergency intervention request; based on the emergency intervention request, reducing or terminating the final power command currently issued to the PCS, wherein the emergency intervention request is a derating or shutdown request.

[0065] Specifically, this application first monitors the voltage change rate and / or temperature rise rate of the battery in real time. This step aims to capture rapid, microscopic changes in the internal state of the battery, which are often early warning signals of battery anomalies (such as thermal runaway, localized overheating, and sudden voltage drops in individual cells). By monitoring voltage and temperature at high frequency and continuously, potential safety hazards can be detected in a timely manner. Specifically, the voltage and temperature of individual battery cells or modules can be sampled in real time using voltage and temperature sensors integrated within the BMS, thereby calculating the voltage change rate and temperature rise rate. Secondly, when the voltage change rate and / or temperature rise rate exceed a preset safety threshold, an emergency intervention request is generated. This step is a key mechanism for triggering a safety response. The preset safety threshold is the basis for determining whether the battery state is abnormal and requires emergency intervention. When the monitored voltage change rate or temperature rise rate exceeds these thresholds, it indicates that the battery may be in a dangerous state and immediate measures are needed. The safety threshold can be set based on various factors such as the battery's chemical type, capacity, state of health (SOH), state of charge (SOC), and ambient temperature, through extensive experimental data analysis, simulation, or expert experience. These thresholds can be fixed values ​​or dynamically adjusted according to the battery's actual operating conditions and aging degree. Once the monitored value exceeds the threshold, the system immediately generates an emergency intervention request. This request can be a specific signal, a data packet, or a status flag, sent to the control system through an internal communication mechanism, indicating that immediate safety measures are required.

[0066] Finally, based on the emergency intervention request, the final power command currently issued to the PCS is reduced or terminated. This step is the specific execution method of the emergency intervention request, aiming to quickly alleviate the abnormal state of the battery and prevent the accident from escalating by directly controlling the power output of the PCS. When an emergency intervention request is received, the control system will take corresponding actions according to the type of request (derating or shutdown). A derating request means maintaining the system operation as much as possible while ensuring safety. For example, if the battery shows slight signs of overheating, the system may reduce the final power command currently issued to the PCS by 25% or 10% to reduce the battery's charging and discharging current, thereby reducing heat generation. This method can provide the battery with a recovery period without completely interrupting service. A shutdown request is the highest level of intervention measure taken when the battery state is extremely dangerous. For example, when the rate of temperature rise or voltage change reaches a critical value, indicating an extremely high risk of thermal runaway, the system will immediately set the final power command issued to the PCS to zero, or directly send a shutdown command to the PCS, forcing the energy storage system to stop all charging and discharging operations to protect the battery and system safety to the greatest extent. Upon receiving these instructions, the PCS will quickly adjust its power output to ensure that the battery operates within a safe range or stops operating completely.

[0067] Through the above technical solution, this application further enhances the real-time response capability to abnormal battery microstate conditions in the collaborative optimization and linkage control method for energy storage systems. By monitoring the battery's voltage change rate and / or temperature rise rate in real time and at high frequency, the system can capture early signals of internal battery anomalies. When these micro-changes exceed preset safety thresholds, the system can quickly generate an emergency intervention request and, based on this request, immediately reduce or terminate the final power command currently issued to the PCS. This rapid response mechanism of derating or shutdown effectively avoids serious safety accidents such as overload and thermal runaway caused by the deterioration of battery microstate conditions, thereby improving the operational safety of the energy storage system. At the same time, this refined safety control also extends the battery's lifespan, avoids irreversible damage caused by macro-level commands ignoring the battery's internal state, and ensures that the energy storage system can operate in a safer and more reliable manner while responding to grid demands.

[0068] In one embodiment, this application proposes an energy storage collaborative optimization and linkage control system, such as... Figure 4 As shown, it includes a data acquisition module, a data processing module, a multi-timescale decision-making module, and an adaptive control module integrated into the same collaborative control platform.

[0069] The collaborative control platform is an integrated software or hardware system, implemented as a central controller, distributed control system, or cloud-based platform. As the core control hub of the energy storage system, it is responsible for collecting and processing data from the Battery Management System (BMS) and Energy Storage System (EMS), and issuing decision-making and control commands. This includes data fusion (collecting internal battery state data from the BMS and external demand and planning data from the EMS), collaborative optimization (performing calculations across multiple time scales to find the optimal solution for safety, economy, and lifespan), and command generation (generating final executable commands that take into account the constraints of all parties).

[0070] The uplink data stream consists of high-frequency, high-precision battery internal status data from the BMS to the platform, and longer-term external demand and planning data from the EMS to the platform. The optimized safety power command from the platform to the PCS is the final output of collaborative decision-making, ensuring both safety and economy in execution. The downlink data stream includes control mode or parameter tuning commands from the platform to the BMS (switching equalization strategies, adjusting SOC estimation parameters), providing optimized guidance for battery internal management. The downlink data stream includes planning curve correction and cost feedback from the platform to the EMS, forming a closed loop to help the EMS develop better plans for the future.

[0071] The data acquisition module collects internal status data such as battery cell voltage, temperature, SOC, and SOH from the BMS at fixed intervals via CAN bus or Modbus protocol. Simultaneously, it receives external demand and planning data such as grid dispatch instructions, electricity price curves, and load forecasts from the EMS. Based on the collected data and a preset battery safety threshold model, the data processing module calculates the maximum allowable charging power and maximum allowable discharging power under the current battery condition in real time, forming a dynamically optimal operating power range.

[0072] The multi-timescale decision-making module, based on the scheduling plan and second-level grid frequency regulation requirements, integrates and optimizes external demand data with the optimal operating power range on a daily scale. On a minute-level scale, it prioritizes matching grid scheduling commands; on a second-level scale, the module rapidly corrects power commands based on battery micro-state change data (such as instantaneous fluctuations in single-cell voltage exceeding 0.1V / s), ensuring that the commands remain within safe boundaries. After the adaptive control module sends the final power command to the PCS, it continuously monitors real-time data from the BMS. If it detects a rise in battery temperature exceeding 3°C within 10 seconds or a sudden increase in internal resistance of 10%, it immediately reduces the discharge power by 20% or switches to charging mode, achieving closed-loop dynamic adjustment of the commands.

[0073] Through the above technical solutions, this application realizes deep data interaction and real-time collaborative decision-making among BMS, PCS and EMS, forming a closed-loop control system of "decision-execution-feedback-re-decision". This system can dynamically balance the battery safety boundary and grid demand, optimize charging and discharging strategies while ensuring battery life, reduce the risk of thermal runaway caused by overload, and improve the response accuracy and speed to grid frequency regulation and peak shaving commands, thereby enhancing the overall economy, safety and grid support capabilities of the energy storage system.

[0074] In one embodiment, this application further proposes that the collaborative control platform is also used to issue control commands to the BMS based on the battery's internal state data to achieve optimized guidance for the battery's internal management. The control commands include switching the equalization strategy and / or adjusting the SOC estimation parameters.

[0075] Specifically, issuing control commands to the BMS refers to the collaborative control platform generating and sending specific operation commands to the BMS based on its optimization decisions. This aims to guide the BMS to adjust its internal management strategies to achieve a better battery operating state. The optimized guidance for internal battery management refers to the proactive intervention of the collaborative control platform, enabling the BMS's battery management functions (such as balancing, SOC estimation, and thermal management) to operate more intelligently and efficiently, adapting to the overall operating goals and external demands of the energy storage system. The control commands include switching balancing strategies, where the collaborative control platform, based on battery internal state data (such as imbalance), instructs the BMS to switch from one balancing mode (such as passive balancing) to a more aggressive balancing mode (such as active balancing), or adjusts the parameters of the current balancing strategy (such as balancing current and balancing threshold). Furthermore, the control commands may also include adjusting SOC estimation parameters, where the collaborative control platform dynamically corrects the BMS's SOC estimation model based on battery internal state data (such as internal resistance changes, temperature drift, and SOH decay) to improve the accuracy of SOC estimation.

[0076] Through the aforementioned technical solution, the collaborative control platform can proactively issue refined control commands to the BMS based on real-time battery internal state data, achieving deep interaction between the collaborative control platform and the BMS. This deep interaction enables the collaborative control platform to more accurately assess the actual operating status and potential risks of the battery pack and perform targeted optimizations. This optimization guidance ensures that the BMS's internal management strategy remains consistent with the macro-operational goals and external demands of the energy storage system, allowing the battery pack to receive refined protection and management while meeting grid dispatch commands. This maximizes the battery's economic benefits and lifespan while ensuring safety.

[0077] In one embodiment, this application further proposes that the collaborative control platform is also used to feed back the correction results of the planning curve and / or actual operating cost data to the EMS based on external demand and planning data, so as to optimize the future scheduling plan of the EMS.

[0078] Specifically, while performing its core functions (such as data acquisition, processing, decision-making, and control), the collaborative control platform continuously receives and processes external demand and planning data from the EMS. Based on this raw scheduling input data, the collaborative control platform can establish a benchmark corresponding to the EMS scheduling logic for subsequent feedback and optimization. For example, the collaborative control platform can receive scheduling instructions, electricity price information, and load forecast data issued by the EMS in real time through the data acquisition module and store them in an internal database as a reference for subsequent feedback and optimization decisions. On this basis, the collaborative control platform sends back to the EMS the adjustments it makes to the original planning instructions from the EMS during actual operation (i.e., the correction results of the planning curve) and the economic benefits or cost data generated by the actual operation of the energy storage system. The correction results of the planning curve refer to the fact that, during multi-timescale decision-making, the collaborative control platform may limit or adjust the macroscopic planning curve issued by the EMS based on the actual state of the battery (such as the optimal operating power range) to ensure safety and battery life. The difference between these adjusted actual execution curves and the original planning curve of the EMS is the correction result. Actual operating cost data includes the actual charging and discharging costs, battery loss costs (calculated based on the SOH attenuation model), and ancillary service revenue incurred by the energy storage system during the execution of scheduling commands. After receiving the correction results and actual operating cost data from the collaborative control platform, the EMS no longer blindly schedules according to the preset model. Instead, it can use this real and detailed operating data to iteratively optimize its scheduling algorithm and model, thereby optimizing the EMS's future scheduling plans.

[0079] Through the aforementioned technical solution, the collaborative control platform feeds back the EMS (Energy Management System) with corrections to the EMS plan and actual operating cost data during operation, enabling the EMS to shift from macro-level scheduling to refined, adaptive scheduling. The EMS no longer relies solely on preset models and predictive data but can learn and adjust based on the actual state and operational performance within the energy storage system. This closed-loop feedback mechanism improves the accuracy, economy, and adaptability of the EMS's future scheduling plans, avoids resource waste and inefficiency, and ultimately maximizes the overall operational efficiency and economic benefits of the energy storage system.

[0080] In one embodiment, this application proposes an improvement, namely, establishing a direct communication link between the collaborative control platform and the BMS for transmitting battery microstate change data and emergency intervention requests.

[0081] Specifically, this direct communication link is a dedicated, high-efficiency data transmission channel established between the collaborative control platform and the BMS, with its core focus on reducing data transmission latency and improving communication reliability. In practical implementation, this direct communication link can be implemented using various technologies. For example, a dedicated wired communication link can be used, such as a direct connection based on CAN bus (Controller Area Network) or Industrial Ethernet; high-priority wireless communication technologies can also be employed. The battery microstate change data transmitted through this direct communication link refers to parameters that can reflect the battery's internal operating status in real time and with precision, such as the battery's voltage change rate and temperature rise rate. This data plays a crucial role in the early detection of battery anomalies and the assessment of immediate safety risks.

[0082] By establishing a direct communication link between the collaborative control platform and the BMS through the above technical solution, it is ensured that data on changes in the battery's microstate can be transmitted to the collaborative control platform in real time with extremely low latency. This allows the adaptive control module to detect subtle battery anomalies more promptly. Simultaneously, emergency intervention requests can also be rapidly delivered through this dedicated channel, enabling the adaptive control module to immediately and dynamically adjust the final power command. This rapid response mechanism significantly enhances the safety margin of the energy storage system, ensuring the stable operation and lifespan of the battery system.

[0083] It should be noted that the information interaction and execution process between the above modules / units are based on the same concept as the method embodiments of this application. Their specific functions and technical effects can be found in the method embodiments section, and will not be repeated here. The descriptions of each embodiment have their own emphasis; parts not detailed or recorded in a particular embodiment can be found in the relevant descriptions of other embodiments.

[0084] The following example, using a certain energy storage power station participating in the grid's "peak shaving and valley filling" and "primary frequency regulation" services, will be used to specifically explain the control method of this application.

[0085] 1. Initialization: The collaborative control platform starts up and reads relevant parameters, including battery type, rated capacity, initial SOH value, life decay model, etc.

[0086] 2. Long-term scale decision (EMS): EMS generates a planned charge and discharge curve (P_sch) based on the peak and off-peak electricity prices of the next day (e.g., charging during off-peak hours after 10 pm and discharging during peak hours at 10 am).

[0087] 3. Short-to-medium timescale correction (collaborative platform): The platform obtains real-time data from the BMS. The current average SOC of the battery pack is 80%, but the difference between the maximum and minimum SOC is 5%. The temperature is normal, and the SOH is 92%. The power capability assessment module calculates: Considering the slightly high SOC imbalance, to protect weaker batteries, the maximum discharge power is limited from the rated value of 1C to 0.8C, i.e., power capability (P_max_ch / dis) = 0.8 * rated capacity. At this time, if the required discharge power in P_sch is 0.9C, the collaborative platform corrects it to P_SAFE = 0.8C.

[0088] 4. Real-time Adaptive Control (PCS and BMS Linkage): The PCS receives the command P_SAFE=0.8C and executes discharge. When the grid frequency fluctuates, it requires energy storage to provide primary frequency regulation power support. The collaborative platform receives an emergency command to increase power by 0.1C. The platform immediately queries the BMS for real-time capabilities. The BMS detects that under 0.8C discharge, the temperature of one battery module is rising rapidly (dT / dt exceeds the standard). The BMS sends a "recommendation derating" signal to the platform. After comprehensive judgment, the collaborative platform rejects the grid's additional power demand and proactively reduces the current discharge power from 0.8C to 0.7C to prevent the module from overheating.

[0089] Feedback and Learning: This event was documented and will be used to optimize future lifetime degradation models and power assessment algorithms.

[0090] It is understood that those skilled in the art will clearly recognize that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0091] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0092] This application provides a computer program product, including a computer program, which, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments above.

[0093] In applications, the memory may be an internal storage unit of an electronic device in some embodiments, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the electronic device, such as a plug-in hard drive, smart memory card (SMC), secure digital storage (SD) card, flash memory card, etc. Furthermore, the memory may include both internal and external storage units of the electronic device. The memory is used to store operating systems, applications, bootloaders, data, and other programs, such as program code for computer programs. The memory can also be used to temporarily store data that has been output or will be output.

[0094] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or some intermediate form. Computer-readable media can include at least: any entity or device capable of carrying computer program code to an electronic device, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0095] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0096] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application. Content not described in detail in this specification belongs to the prior art known to those skilled in the art.

Claims

1. A method for coordinated optimization and linkage control of energy storage, characterized in that: Real-time acquisition of battery internal status data and external demand and planning data; The optimal operating power range of the energy storage system is dynamically calculated based on the internal state data of the battery. Based on the external demand data and the optimal operating power range, collaborative optimization decisions are made at multiple time scales to generate the final power command. The final power command is sent to the PCS for execution, and during the execution process, the final power command is dynamically adjusted based on the micro-state change data of the battery.

2. The energy storage collaborative optimization and linkage control method and system according to claim 1, characterized in that: The battery internal state data includes at least one of the following: cell voltage, cell temperature, SOC, SOH, imbalance, and internal resistance; the external demand data includes at least one of the following: grid dispatch instructions, electricity price, and load forecast.

3. The energy storage collaborative optimization and linkage control method according to claim 1, characterized in that: The dynamic calculation of the optimal operating power range of the energy storage system based on the internal state data of the battery includes: Based on the battery's internal state data, calculate the five major boundary constraints; The limiting current corresponding to each boundary constraint is determined based on the five major boundary constraints. The final safety boundary is determined by merging and intersecting the various limiting currents. The optimal operating power range is determined based on the safety boundary.

4. The energy storage collaborative optimization and linkage control method according to claim 3, characterized in that: The five boundary constraints are voltage constraint, temperature constraint, hardware constraint, lifetime decay constraint, and consistency and security constraint. The limiting currents corresponding to each boundary constraint are: the instantaneous charge and discharge current determined based on the limiting voltage; the current corresponding to the allowable heat generation power determined based on the limiting temperature; the maximum hardware current determined based on the PCS and connector ratings; the healthy current allowed by the control attenuation rate determined based on SOH and SOC; and the current allowed to prevent the deterioration of inconsistency determined based on the SOC imbalance.

5. The energy storage collaborative optimization and linkage control method according to claim 1, characterized in that: The step of performing collaborative optimization decisions across multiple time scales based on the external demand data and the optimal operating power range to generate the final power command includes: At the first time scale, based on electricity prices and load forecasts, a planned charge and discharge power curve is generated with the goal of optimal economic efficiency. On the second time scale, the planned charge and discharge power curve is compared and corrected with the optimal operating power range. If the planned charge and discharge power curve exceeds the optimal operating power range, the planned charge and discharge power curve is limited to the optimal operating power range, and a safe power command is generated; otherwise, a safe power command is generated directly based on the planned charge and discharge power curve. In the third time scale, the secure power command is used as the final power command; The first time scale is at the hour or day level, the second time scale is at the minute or second level, and the third time scale is at the millisecond or second level.

6. The energy storage collaborative optimization and linkage control method according to claim 1, characterized in that: The step of dynamically adjusting the final power command based on the battery's microstate change data includes: Real-time monitoring of battery voltage change rate and / or temperature rise rate; When the voltage change rate and / or temperature rise rate exceed a preset safety threshold, an emergency intervention request is generated; Based on the emergency intervention request, reduce or terminate the final power command currently issued to the PCS.

7. A collaborative optimization and linkage control system for energy storage, characterized in that: This includes a data acquisition module, a data processing module, a multi-timescale decision-making module, and an adaptive control module integrated into the same collaborative control platform. The data acquisition module is used to acquire battery internal status data from BMS and external demand and planning data from EMS in real time. The data processing module is used to dynamically calculate the optimal operating power range of the energy storage system based on the internal state data of the battery. The multi-timescale decision module is used to make collaborative optimization decisions on multiple timescales based on the external demand data and the optimal operating power range, and generate the final power command. The adaptive control module is used to send the final power command to the PCS for execution. During the execution process, the final power command is dynamically adjusted based on the micro-state change data of the battery fed back by the BMS in real time.

8. The energy storage collaborative optimization and linkage control system according to claim 7, characterized in that: The collaborative control platform and the BMS are connected by a direct communication link for transmitting battery microstate change data and emergency intervention requests.

9. An electronic device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it causes the electronic device to implement the energy storage collaborative optimization linkage control method as described in any one of claims 1-6.

10. A computer-readable storage medium storing a computer program thereon, characterized in that: When the computer program is executed by the processor, it implements the energy storage collaborative optimization linkage control method as described in any one of claims 1 to 6.

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