Multi-physical quantity constraint collaborative operation control method and system for multi-element fusion energy storage power station

Abstract

The present application relates to a kind of multi-physical quantity constraint collaborative operation control method and system for multi-element fusion energy storage power station, method includes: the operation physical quantity of each type of energy storage unit in multi-element fusion energy storage power station is collected;Respectively, the callable ability, safety margin and life consumption factor of each energy storage unit in current control period are calculated;Identify the operation task type in current control period and future control period, and then determine operation task demand;The adaptation degree of each energy storage unit to operation task demand in current control period and future control period is calculated, to adjust the adaptation weight of each energy storage unit, obtain the multiple candidate power distribution scheme of overall multi-element fusion energy storage power station;Each candidate power distribution scheme is based on the constraint of multiple physical quantities Comprehensive constraint check, to generate the control instruction of multi-element fusion energy storage power station.Compared with prior art, the present application improves the accuracy of power distribution and the utilization efficiency of multi-type energy storage resources.

Description

Technical Field

[0001] This invention relates to the field of energy storage operation control technology, and in particular to a multi-physical quantity constraint collaborative operation control method and system for multi-element integrated energy storage power stations. Background Technology

[0002] With the continuous advancement of new power system construction and the increasing scale of new energy installations, energy storage power stations are evolving from single energy storage configurations to multi-functional integrated energy storage. Multi-functional integrated energy storage power stations are typically composed of multiple energy storage systems such as lithium iron phosphate batteries, semi-solid-state lithium batteries, and vanadium redox flow batteries, and participate in peak shaving, frequency regulation, backup support, new energy consumption, and grid connection point power smoothing through energy management systems (EMS) and power conversion systems (PCS).

[0003] Different energy storage technologies exhibit significant differences in power response speed, sustainable discharge duration, cycle life, thermal characteristics, safety margins, and operational constraints. Lithium iron phosphate batteries are suitable for intraday peak shifting, short-term peak shaving, and routine charge-discharge tasks; semi-solid-state lithium batteries have better power response capabilities and are suitable for rapid frequency regulation and backup support tasks; vanadium redox flow batteries are suitable for long-term charge-discharge, high-frequency cycling, and continuous peak shaving tasks. Therefore, various types of energy storage systems cannot be simply deployed uniformly according to rated power or a fixed ratio, but should be dynamically matched based on their current physical state and task requirements. For example, the invention disclosed in publication number CN120810740A discloses a method and system for coordinated control of energy storage power stations. The specific steps of the method are as follows: Ⅰ. During the operation of the energy storage power station, the status data of each energy storage unit in the energy storage power station is collected and preprocessed in real time through edge nodes and uploaded to the central coordinator; Ⅱ. The central coordinator dynamically constructs a virtual energy storage cluster based on the current task requirements and the status of the energy storage units, and performs hybrid control of the energy storage power station; Ⅲ. Based on the real-time preprocessed status data, the dominant operating scenario is automatically determined, and the weights of each energy storage unit in the energy storage power station are dynamically updated. This scheme only assesses the adaptability of each energy storage unit to the current grid task, i.e., after reaching a healthy state, and then adjusts the operating weights. This is a post-event adjustment and optimization, and cannot perform advance coordinated control.

[0004] Existing energy storage power station operation and control methods mostly adopt fixed operation strategies, manual experience-based scheduling, or single economic indicator ranking. In some schemes, the system mainly generates operation strategies based on economic parameters such as electricity price, revenue, cost, or subsidies. Although this can reflect certain operational value, it easily ignores the actual physical state of the energy storage unit and makes it difficult to guarantee the safety, executability, and lifespan friendliness of the strategy under complex operating conditions.

[0005] Existing technologies still suffer from insufficient perception of the physical state of energy storage units. Most systems primarily collect SOC, total power, and alarm status, without fully integrating physical quantities such as terminal voltage, individual cell voltage, individual cell voltage difference, inter-cluster temperature difference, maximum temperature, minimum temperature, SOH, internal resistance, cycle count, charge / discharge rate, available capacity, and PCS response time. For vanadium redox flow batteries, there is often a lack of coordinated judgment on electrolyte flow rate, pressure, stack temperature, and pump operating status. Consequently, the system struggles to accurately calculate the available power, usable energy, sustainable discharge duration, and safety margin of each energy storage unit within the current cycle.

[0006] In actual operation, if control is based solely on market returns or fixed allocation ratios, high-yield tasks may concentrate on a particular type of energy storage unit, leading to localized deep charging and discharging of batteries, increased temperature rise, widened voltage differential, increased internal resistance, or accelerated decline in state of equilibrium (SOH). It may also result in insufficient response speed, premature energy depletion, or increased power deviation at the grid connection point in scenarios involving rapid frequency regulation, backup support, or long-term peak shaving. Essentially, these problems stem from the lack of a unified matching mechanism between the differences in the physical characteristics of various types of energy storage systems and the dynamic operational demands. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology that lacks consideration of the differences in physical characteristics of multiple types of energy storage systems, and to provide a multi-physical quantity constraint collaborative operation control method and system for multi-element integrated energy storage power stations.

[0008] The objective of this invention can be achieved through the following technical solutions: A multi-physical quantity constraint collaborative operation control method for multi-functional integrated energy storage power stations, wherein the multi-functional integrated energy storage power station includes various types of energy storage units, the method comprising the following steps: Collect and preprocess the operational physical quantities of various types of energy storage units in a multi-functional integrated energy storage power station; Based on the preprocessed operational physical quantities, calculate the available capacity, safety margin, and lifetime loss factor of each energy storage unit within the current control cycle. Based on the grid dispatch instructions transmitted to the multi-functional integrated energy storage power station, as well as the current operating status and future operating prediction status of the multi-functional integrated energy storage power station, the types of operating tasks in the current control cycle and future control cycles are identified, and then the operating task requirements are determined. Based on the available capacity, safety margin, and lifetime loss factor of each energy storage unit, the adaptability of each energy storage unit to the operational task requirements in the current control cycle and future control cycles is calculated, thereby adjusting the adaptability weight of each energy storage unit. Based on the adaptation weight, safety margin and lifetime loss factor of each energy storage unit, the operating parameters of each energy storage unit are generated, and multiple candidate power allocation schemes for the overall multi-element integrated energy storage power station are obtained. The candidate power allocation schemes are comprehensively constrained based on multiple physical quantities. The final power allocation scheme is determined based on the candidate power allocation schemes that pass the comprehensive constraint verification, so as to generate the control commands for the multi-element integrated energy storage power station.

[0009] Furthermore, the operating physical quantities include terminal voltage, individual cell voltage, individual cell voltage difference, inter-cluster voltage difference, charging and discharging current, maximum temperature, minimum temperature, temperature difference, SOC, SOH, internal resistance, available capacity, rated power, maximum charging and discharging power, charging and discharging efficiency, cumulative cycle count, PCS response time, alarm status, and maintenance status. If the energy storage unit of the multi-element integrated energy storage power station includes a vanadium redox flow battery system, the corresponding operating physical quantities of the vanadium redox flow battery system also include electrolyte flow rate, pipeline pressure, electrolyte temperature, stack temperature, pump operating status, and stack power status.

[0010] Furthermore, the preprocessing includes: filling in missing values, removing outliers, unifying units, aligning sampling times, and verifying boundaries for the collected physical quantities. The boundary verification includes: if the collected operating physical quantities exceed the corresponding preset reasonable range, then reduce the adaptation weight of the corresponding energy storage unit or remove the corresponding energy storage unit.

[0011] Furthermore, the callable capabilities include rechargeable power, dischargeable power, available energy, continuous discharge duration, power ramping capability, and response time; The safety margins include SOC margin, temperature margin, voltage consistency margin, power margin, SOH margin, internal resistance margin, and fluid system pressure margin. The lifetime loss factor is calculated based on the depth of charge / discharge, charge / discharge rate, temperature level, number of cycles, SOH change rate, and internal resistance growth rate.

[0012] Furthermore, the current operating status of the multi-functional integrated energy storage power station includes grid connection point power deviation, ancillary service demand, and market operation signals; The future operational forecast status includes load forecast results and new energy output forecast results; The indicators for the operational task types include long-term peak shaving, short-term peak shifting, rapid frequency regulation, backup support, renewable energy consumption, peak shaving and valley filling, grid connection point power smoothing, and market response. The operational task requirements include the requirements for power amplitude, energy capacity, response time, duration, and power ramp rate for each operational task type.

[0013] Furthermore, the method calculates the adaptability of each energy storage unit to the operational task requirements based on the available power, available energy, response speed, continuous discharge duration, power ramp-up rate, safety margin, lifetime loss factor, and current SOC range of each energy storage unit. The operating parameters of the energy storage unit include target charge / discharge power, execution period, target SOC range, reserve capacity, and power ramp-up rate.

[0014] Furthermore, the physical quantity constraints of the comprehensive constraint verification include: upper and lower limits of SOC, temperature limit, single-cell pressure difference limit, inter-cluster temperature difference limit, lower limit of SOH, internal resistance growth limit, fluid flow pressure limit, PCS power limit, grid connection point power limit, power ramp limit, and scheduling commitment constraints. In the process of comprehensive constraint verification, revenue evaluation factors, electricity price signals, ancillary service value and operating losses are also set as auxiliary evaluation factors to evaluate each candidate power allocation scheme and select the final power allocation scheme.

[0015] Furthermore, the control commands of the multi-energy storage power station include: the charging and discharging status of each energy storage unit, target power, execution period, SOC target range, reserve capacity, power ramp-up rate, stop conditions, and protection threshold. The energy storage unit types of the multi-element integrated energy storage power station include lithium iron phosphate battery systems, semi-solid-state lithium battery systems, and vanadium redox flow battery systems.

[0016] Furthermore, the method also includes continuously comparing the target power with the actual power, the predicted power with the actual power, the planned value of SOC with the actual value, the temperature change trend, the SOH change trend, the pressure difference change trend, and the power deviation at the grid connection point after the multi-energy storage power station executes the control command. If the deviation exceeds the corresponding deviation threshold, the callable capacity and adaptability of each energy storage unit are recalculated to correct subsequent control commands.

[0017] This invention also provides a multi-physical quantity constraint cooperative operation control system based on the multi-physical quantity constraint cooperative operation control method for multi-element integrated energy storage power stations described above. The multi-element integrated energy storage power station includes various types of energy storage units, and the system includes: The physical quantity acquisition module is used to acquire and preprocess the physical quantities of various types of energy storage units in the multi-electrode integrated energy storage power station. The callable capacity calculation module is used to calculate the callable capacity of each energy storage unit in the current control cycle based on the preprocessed operating physical quantities. The safety life assessment module is used to calculate the safety margin and life loss factor of each energy storage unit in the current control cycle based on the pre-processed operating physical quantities. The operation requirement identification module is used to identify the operation task type in the current control cycle and the future control cycle based on the grid dispatch instructions transmitted to the multi-electrode integrated energy storage power station, as well as the current operation status and future operation prediction status of the multi-electrode integrated energy storage power station, and then determine the operation task requirements. The multi-energy storage task adaptation module is used to calculate the adaptability of each energy storage unit to the operational task requirements in the current control cycle and future control cycles based on the available capacity, safety margin and life loss factor of each energy storage unit, thereby adjusting the adaptation weight of each energy storage unit. The collaborative optimization control module is used to generate the operating parameters of each energy storage unit based on the adaptation weight, safety margin and lifetime loss factor of each energy storage unit, and obtain multiple candidate power allocation schemes for the overall multi-element integrated energy storage power station. The strategy interface module is used to perform comprehensive constraint verification on each candidate power allocation scheme based on constraints of multiple physical quantities, and determine the final power allocation scheme based on the candidate power allocation schemes that pass the comprehensive constraint verification, so as to generate control commands for the multi-element integrated energy storage power station. The closed-loop feedback correction module is used to continuously compare the target power with the actual power, the predicted power with the actual power, the planned value of SOC with the actual value, the temperature change trend, the SOH change trend, the pressure difference change trend, and the power deviation at the grid connection point after the multi-energy storage power station executes the control command. If the deviation exceeds the corresponding deviation threshold, the available capacity and adaptability of each energy storage unit are recalculated to correct subsequent control commands.

[0018] Compared with the prior art, the present invention has the following advantages: (1) This invention no longer uses price, cost and revenue as the sole basis for control, but instead uses physical quantities such as voltage, current, temperature, SOC, SOH, internal resistance, differential pressure, flow rate, pressure, response time and available capacity as the basis for control. The system calculates the callable capacity and task adaptability through physical quantities, so that the control scheme is based on measurable, verifiable and feedback technical parameters, and transforms economic rules into physical state-driven technical control.

[0019] (2) Existing systems often estimate energy storage capacity based on rated power and SOC, which cannot reflect real-time changes in temperature, pressure difference, SOH and liquid flow pressure. This invention is based on the dynamic callable capability calculated by multiple physical quantities, which can more accurately obtain the current chargeable and dischargeable power, available energy and sustainable discharge duration of each energy storage unit, thereby improving the accuracy of power allocation.

[0020] (3) This invention incorporates depth of charge / discharge, charge / discharge rate, temperature, number of cycles, SOH change rate, and internal resistance growth rate into the lifespan degradation evaluation. For energy storage units with high temperature, large pressure difference, low SOH, or significant internal resistance growth, the system automatically reduces the call ratio, which helps to reduce excessive cycling of local batteries, increased temperature rise, and rapid lifespan degradation.

[0021] (4) According to the power, energy, response speed and duration requirements of different tasks, the present invention performs task matching in combination with the physical characteristics of each energy storage unit, so that the vanadium redox flow battery takes priority to undertake long-term peak shaving and continuous charge and discharge tasks, the lithium iron phosphate battery takes priority to undertake intraday peak shifting and conventional peak shaving tasks, and the semi-solid lithium battery takes priority to undertake fast frequency regulation and backup support tasks, thereby improving the utilization efficiency of multiple types of energy storage resources and the rationality of matching multiple types of energy storage tasks.

[0022] (5) When generating control commands, the present invention simultaneously considers the PCS power limit, power ramp rate, grid connection point power limit and actual execution deviation, and performs rolling correction through closed-loop feedback, which can reduce the deviation between the target power and the actual power and improve the response stability of the energy storage power station to dispatch commands and new energy fluctuations.

[0023] (6) This invention retains the benefit evaluation factors as auxiliary evaluation content, but they must comply with physical constraints such as SOC, temperature, pressure difference, SOH, internal resistance, liquid flow pressure and grid-connected power. As a result, the system can improve the energy storage operation efficiency and the overall life cycle operation effect within the safety boundary, while also taking into account the operation economy.

[0024] (7) The present invention forms a closed-loop mechanism of physical quantity acquisition, callable capability calculation, safety life evaluation, task adaptation, collaborative power allocation, control command issuance and execution feedback correction, which is convenient for deployment, verification and iteration in engineering systems and has good practical application value. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a multi-physical quantity constraint-based collaborative operation control method for multi-element integrated energy storage power stations provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a multi-physical quantity constraint collaborative operation control system for a multi-element integrated energy storage power station provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0028] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0029] Example 1 like Figure 1 As shown in the figure, this embodiment provides a multi-physical quantity constraint collaborative operation control method for multi-functional integrated energy storage power stations. The multi-functional integrated energy storage power station includes various types of energy storage units. This method does not use price, cost or benefit as the sole optimization basis, but uses the physical state of the energy storage unit as the control basis. By collecting the operating physical quantities of multiple types of energy storage systems, it calculates the callable capacity, safety margin, lifetime loss and task adaptability, and then generates unit-level power allocation scheme and EMS executable control commands.

[0030] It should be noted that in this invention, the revenue evaluation factor, electricity price signal, and market signal can serve as auxiliary inputs for operational demand identification and strategy ranking, but they must be included in the calculation along with physical constraints such as SOC boundary, temperature boundary, voltage consistency, SOH decay, internal resistance change, fluid pressure, PCS power limit, and grid connection point power limit. Candidate schemes that do not meet the safety boundary, lifetime boundary, or power boundary, even if they have a high economic evaluation, will not be included in the executable strategy set.

[0031] The method includes the following steps: S1: Collect and preprocess the operational physical quantities of various types of energy storage units in a multi-functional integrated energy storage power station; For the acquisition of operational physical quantities, this embodiment obtains data from the BMS, PCS, EMS, SCADA system, flow battery control system, grid dispatch system, forecasting system, and market information system. These energy storage physical quantities include terminal voltage, cell voltage, cell voltage difference, inter-cluster voltage difference, charge / discharge current, maximum temperature, minimum temperature, temperature difference, SOC, SOH, internal resistance, available capacity, rated power, maximum charge / discharge power, charge / discharge efficiency, cumulative cycle count, PCS response time, alarm status, and maintenance status. For the vanadium redox flow battery system, this also includes electrolyte flow rate, pipeline pressure, electrolyte temperature, stack temperature, pump operating status, and stack power status. The system performs time synchronization according to 5-minute, 15-minute, or other preset control cycles to form a unified operational status dataset.

[0032] For the preprocessing of operational physical quantities, this embodiment performs missing value imputation, outlier removal, unit standardization, sampling time alignment, and boundary verification on the collected data. For data with parameters such as voltage, current, temperature, SOC, SOH, flow rate, and pressure exceeding reasonable ranges, anomalies are marked; for energy storage units in alarm, maintenance, communication abnormality, or SOC exceeding limit states, their callability weight is reduced or they are temporarily excluded.

[0033] S2: Calculate the available capacity of each energy storage unit in the current control cycle based on the preprocessed physical quantities of operation; Specifically, the set energy storage unit's available capacity index is configured. The determination process includes: calculating the rechargeable power, dischargeable power, available energy, sustainable discharge duration, power ramp-up capability, and response time of each energy storage unit in the current control cycle based on SOC, SOH, temperature, pressure difference, internal resistance, PCS power limit, flow system flow and pressure status, and alarm status.

[0034] The higher the value, the more suitable the energy storage unit is for undertaking the corresponding task in the current cycle.

[0035] In addition, the callable capabilities are not fixed capabilities, but dynamic capabilities that are corrected by real-time physical conditions.

[0036] S3: Based on the preprocessed operating physical quantities, calculate the safety margin and lifetime loss factor of each energy storage unit in the current control cycle; Specifically, the established safety margin evaluation indicators for energy storage units It includes at least SOC margin, temperature margin, voltage consistency margin, power margin, SOH margin, internal resistance margin, and fluid system pressure margin, which can be obtained by weighting the above indicators.

[0037] Simultaneously, the lifetime loss factor is calculated based on the depth of charge / discharge, charge / discharge rate, temperature level, number of cycles, SOH change rate, and internal resistance growth rate. .

[0038] When a certain energy storage unit has a large depth of charge and discharge, a high temperature, or a rapid decrease in state of equilibrium (SOH), Increasing the priority of the system for handling high-frequency cyclic tasks will lower its priority.

[0039] Different weights are assigned to different types of energy storage units to reflect the differences in physical characteristics between lithium iron phosphate batteries, semi-solid lithium batteries, and vanadium redox flow batteries.

[0040] S4: Based on the grid dispatch instructions transmitted to the multi-functional integrated energy storage power station, as well as the current operating status and future operating prediction status of the multi-functional integrated energy storage power station, identify the types of operating tasks in the current control cycle and future control cycles, and then determine the operating task requirements. In this embodiment, based on grid dispatch instructions, grid connection point power deviation, load forecasting, renewable energy output forecasting, ancillary service demand, and market operation signals, the types of operational tasks within the current and future control cycles are identified. These task types include at least long-term peak shaving, short-term peak shifting, rapid frequency regulation, reserve support, renewable energy consumption, peak shaving and valley filling, grid connection point power smoothing, and market response. The requirements for power amplitude, energy capacity, response time, duration, and power ramp-up rate for each type of task are further determined.

[0041] S5: Based on the available capacity, safety margin, and lifespan loss factor of each energy storage unit, calculate the adaptability of each energy storage unit to the operational task requirements in the current control cycle and future control cycles, and adjust the adaptability weight of each energy storage unit accordingly. In this embodiment, the adaptability of each energy storage unit to different operational tasks is calculated based on its available power, usable energy, response speed, sustainable discharge duration, power ramp-up rate, safety margin, lifetime loss factor, and current SOC range. Tasks with long continuous discharge times and low power change frequencies increase the suitability weight of vanadium redox flow batteries; intraday peak shifting, short-term peak shaving, and regular charge-discharge tasks increase the suitability weight of lithium iron phosphate batteries; and tasks with fast frequency modulation, instantaneous power support, and backup response increase the suitability weight of semi-solid-state lithium batteries.

[0042] That is, task adaptability This is used to represent the degree of matching between the i-th energy storage unit and the k-th type of operational task. For long-term peak shaving tasks, Primarily affected by available energy and sustainable discharge duration; for fast frequency modulation tasks, It is mainly affected by response time, power ramp-up capability, and SOC reserve margin; for the task of renewable energy consumption, It is mainly affected by rechargeable power, SOC uplink capacity, and available capacity.

[0043] S6: Based on the adaptation weight, safety margin and lifetime loss factor of each energy storage unit, the operating parameters of each energy storage unit are generated to obtain multiple candidate power allocation schemes for the overall multi-element integrated energy storage power station. In this embodiment, based on task suitability, safety margin, and lifetime loss evaluation results, the system generates the target charge / discharge power, execution period, target SOC range, reserve capacity, and power ramp-up rate for each energy storage unit. If an energy storage unit experiences excessively rapid temperature rise, widening pressure difference, increased internal resistance, SOC approaching the boundary, or rapid SOH drop, the system automatically reduces its utilization rate and transfers some tasks to energy storage units with better conditions or greater suitability.

[0044] S7: Perform comprehensive constraint verification on each candidate power allocation scheme based on constraints of multiple physical quantities, determine the final power allocation scheme based on the candidate power allocation schemes that pass the comprehensive constraint verification, and generate control commands for the multi-element integrated energy storage power station.

[0045] In this embodiment, the constraints for comprehensive constraint verification include at least the upper and lower limits of SOC, temperature limit, single-cell pressure difference limit, inter-cluster temperature difference limit, lower limit of SOH, internal resistance growth limit, fluid flow pressure limit, PCS power limit, grid connection point power limit, power ramp limit, and scheduling commitment constraints.

[0046] Preferably, revenue evaluation factors, electricity price signals, ancillary service value, and operating losses can be used as auxiliary evaluation factors, but physical safety constraints and lifespan constraints must not be exceeded in order to select the final power allocation scheme.

[0047] In the process of generating control commands for a multi-functional integrated energy storage power station, this embodiment transforms the collaborative operation scheme, which undergoes constraint verification, into control commands recognizable by the EMS. These commands include the charging and discharging status of each energy storage unit, target power, execution period, target SOC range, reserve capacity, power ramp-up rate, stop conditions, and protection thresholds. The EMS controls the operation of the PCS and energy storage units based on these commands and returns actual power, SOC changes, temperature changes, voltage changes, fluid pressure changes, equipment alarms, and execution deviations to the closed-loop feedback correction module.

[0048] Preferably, the above method further includes: S8: After the multi-energy storage power station executes the control command, it continuously compares the target power with the actual power, the predicted power with the actual power, the planned value of SOC with the actual value, the temperature change trend, the SOH change trend, the pressure difference change trend, and the power deviation at the grid connection point. If the deviation exceeds the corresponding deviation threshold, the callable capacity and adaptability of each energy storage unit are recalculated to correct subsequent control commands.

[0049] When real-time scheduling demands change, prediction deviations increase, temperature rises too quickly, pressure differentials widen, SOC drops too quickly, fluid flow pressure becomes abnormal, or equipment alarms occur, the system recalculates the available capacity and task adaptability of each energy storage unit and corrects subsequent control commands to form a closed-loop control.

[0050] The following description uses a multi-functional integrated energy storage power station as an example to illustrate the implementation of this invention. This embodiment is only used to illustrate the implementation of this invention; actual engineering parameters can be adjusted according to the power station scale, energy storage type, grid connection requirements, and control cycle.

[0051] This multi-element integrated energy storage power station includes lithium iron phosphate batteries, semi-solid-state lithium batteries, and vanadium redox flow batteries, with a total rated power of 50MW and a total usable capacity of 200MWh. Among them, the lithium iron phosphate batteries have a rated power of 20MW and a capacity of 80MWh; the semi-solid-state lithium batteries have a rated power of 10MW and a capacity of 20MWh; and the vanadium redox flow batteries have a rated power of 20MW and a capacity of 100MWh.

[0052] During the day-ahead phase of a given operating day, the system receives market operation signals such as the following day's load forecast, renewable energy output forecast, peak-shaving demand forecast, ancillary service demand forecast, and time-of-use pricing or spot prices. These market operation signals are only used to assist in identifying operational tasks and evaluating strategies, and are not used as the basis for decisions that violate physical safety boundaries.

[0053] In S101, data is read in 15-minute increments, including current terminal voltage, charge / discharge current, SOC, SOH, maximum temperature, minimum temperature, single-cell voltage difference, inter-cluster temperature difference, internal resistance, cumulative cycle count, PCS maximum charge / discharge power, and alarm status for three types of energy storage. For vanadium redox flow batteries, electrolyte flow rate, pipeline pressure, stack temperature, and pump operating status are further read.

[0054] In S102, the collected data is cleaned and boundary verified. If the highest temperature of a lithium iron phosphate battery cluster reaches the set warning threshold, or the differential pressure of a single cell exceeds the set threshold, the battery cluster is marked as a derating target. If the pressure or pump status of a vanadium redox flow battery branch is abnormal, the available power of that branch is reduced to prevent it from being the primary target of subsequent strategies.

[0055] In S103, the current available capacity of each energy storage unit is calculated. For example, when the lithium iron phosphate battery has a SOC of 65%, a SOH of 94%, a temperature difference within the normal range, and a normal PCS status, it is determined to have good intraday peak shifting capability; when the semi-solid-state lithium battery has a SOC of 70%, a low temperature, and a response time that meets the requirements for fast frequency regulation, it is determined to have good fast response capability; when the vanadium redox flow battery has stable flow rate and pressure, and a sufficient SOC range, it is determined to have good continuous discharge capability.

[0056] In S104, the safety margin and lifetime degradation factor are calculated. If a storage unit's temperature approaches its upper limit, its state of charge (SOC) approaches its lower limit, its internal resistance increases rapidly, or its state of equilibrium (SOH) decreases rapidly, its safety margin decreases and its lifetime degradation factor increases. In subsequent task allocation, the priority of this unit is reduced to avoid accelerated lifetime degradation due to excessive usage.

[0057] In S105, identify the running task. If there is continuous renewable energy consumption or long-term peak-shaving demand between 11:00 and 15:00, the task characteristics are long duration, large energy demand, and relatively low power change frequency; if there is short-term peak load or grid connection point power deviation between 18:00 and 20:00, the task characteristics are short duration and high discharge power; if there is a rapid frequency regulation demand around 19:00, the task characteristics are short response time requirement and high power ramp-up requirement.

[0058] In S106 and S107, task adaptation and coordinated power allocation are performed. For continuous peak shaving tasks from 11:00 to 15:00, vanadium redox flow batteries are prioritized to handle long-term discharge at around 15MW, and the power is adjusted in real time according to flow rate, pressure, and stack temperature. For short-term peak tasks from 18:00 to 20:00, lithium iron phosphate batteries are assigned to handle short-term discharge at higher power. For fast frequency regulation or backup support tasks, semi-solid-state lithium batteries are required to retain a certain SOC and a fast response power margin of around 5MW.

[0059] In S108, a comprehensive constraint verification is performed. If a candidate solution can bring high operational benefits, but causes the SOC of the semi-solid-state lithium battery to fall below the set lower limit, the temperature of the lithium iron phosphate battery to exceed the threshold, or the pressure of the vanadium redox flow battery to be abnormal, then the solution is eliminated. Only solutions that simultaneously meet the constraints of SOC, temperature, pressure difference, SOH, internal resistance, flow pressure, PCS power, and grid connection point power are included in the set of executable strategies.

[0060] In S109, the final collaborative operation scheme is converted into EMS executable control commands. For example, a command is issued to the vanadium redox flow battery to discharge at the target power from 11:00 to 15:00; a command is issued to the lithium iron phosphate battery to discharge briefly from 18:00 to 20:00 and replenish power during low-load periods at night; and a command is issued to the semi-solid-state lithium battery to retain fast response capacity and maintain the SOC between 50% and 80%. The control commands simultaneously include the target power, power ramp rate, target SOC range, stop conditions, and protection thresholds.

[0061] In S110, closed-loop feedback and rolling correction are implemented. If the actual renewable energy output is lower than the predicted value, the renewable energy consumption task demand is recalculated; if the power deviation at the grid connection point increases, the power allocation of each energy storage unit is adjusted; if the temperature of a certain energy storage unit rises too quickly, the pressure difference widens, the SOC drops too quickly, or an equipment alarm occurs, the output of that unit is reduced and the task is reassigned. The operation monitoring platform 109 synchronously displays the physical status, available capacity, task allocation results, control commands, risk warnings, and operational benefits of each energy storage unit, facilitating verification by operation and maintenance personnel.

[0062] Through the above implementation methods, this invention forms a multi-element energy storage collaborative operation control mechanism based on physical quantities, centered on callable capabilities and task adaptability, and with control command execution and feedback correction forming a closed loop. This mechanism can avoid the objective risks and engineering execution risks caused by scheduling solely based on price or revenue, and improve the safe operation, lifespan balancing, power response, and energy utilization efficiency of energy storage power stations.

[0063] Example 2 This embodiment provides a multi-physical quantity constraint collaborative operation control system based on a multi-physical quantity constraint collaborative operation control method for multi-functional integrated energy storage power stations, as described in Embodiment 1. The multi-functional integrated energy storage power station includes various types of energy storage units, and the system includes: The physical quantity acquisition module is used to acquire and preprocess the physical quantities of various types of energy storage units in the multi-electrode integrated energy storage power station. The callable capacity calculation module is used to calculate the callable capacity of each energy storage unit in the current control cycle based on the preprocessed operating physical quantities. The safety life assessment module is used to calculate the safety margin and life loss factor of each energy storage unit in the current control cycle based on the pre-processed operating physical quantities. The operation requirement identification module is used to identify the type of operation task in the current control cycle and the future control cycle based on the grid dispatch instructions transmitted to the multi-electrode integrated energy storage power station, as well as the current operation status and future operation prediction status of the multi-electrode integrated energy storage power station, and then determine the operation task requirements. The multi-energy storage task adaptation module is used to calculate the adaptability of each energy storage unit to the operational task requirements in the current control cycle and future control cycles based on the available capacity, safety margin and life loss factor of each energy storage unit, thereby adjusting the adaptation weight of each energy storage unit. The collaborative optimization control module is used to generate the operating parameters of each energy storage unit based on the adaptation weight, safety margin and lifetime loss factor of each energy storage unit, and obtain multiple candidate power allocation schemes for the overall multi-element integrated energy storage power station. The strategy interface module is used to perform comprehensive constraint verification on each candidate power allocation scheme based on constraints of multiple physical quantities, and determine the final power allocation scheme based on the candidate power allocation schemes that pass the comprehensive constraint verification, so as to generate control commands for the multi-element integrated energy storage power station. The closed-loop feedback correction module is used to continuously compare the target power with the actual power, the predicted power with the actual power, the planned value of SOC with the actual value, the temperature change trend, the SOH change trend, the pressure difference change trend, and the power deviation at the grid connection point after the multi-energy storage power station executes the control command. If the deviation exceeds the corresponding deviation threshold, the available capacity and adaptability of each energy storage unit are recalculated to correct subsequent control commands.

[0064] Preferably, it may also include a monitoring platform for monitoring the system's operating status, control commands, evaluation results, and execution feedback, and displaying physical status, task allocation, risk warnings, operating benefits, and historical statistics through a monitoring interface and operating reports.

[0065] The above modules form a closed-loop connection. The physical quantity acquisition module connects to the BMS, PCS, EMS, SCADA system, flow battery control system, grid dispatching system, and forecasting system; the callable capacity calculation module connects to the physical quantity acquisition module; the safety life assessment module connects to the callable capacity calculation module; the operation demand identification module receives dispatching, forecasting, and market signals; the multi-energy storage task adaptation module connects to the callable capacity calculation module, the safety life assessment module, and the operation demand identification module; the collaborative optimization control module connects to the task adaptation module; the strategy interface module connects to the EMS; the closed-loop feedback correction module receives feedback from the EMS, PCS, and BMS and feeds it back to the relevant modules; and the operation monitoring platform connects to each module to display and store the operation results.

[0066] It should be noted that the specific content and beneficial effects of the system in this application can be found in the above method embodiments, and will not be repeated here.

[0067] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations, wherein the multi-element integrated energy storage power station includes multiple types of energy storage units, characterized in that, The method includes the following steps: Collect and preprocess the operational physical quantities of various types of energy storage units in a multi-functional integrated energy storage power station; Based on the preprocessed operational physical quantities, calculate the available capacity, safety margin, and lifetime loss factor of each energy storage unit within the current control cycle. Based on the grid dispatch instructions transmitted to the multi-functional integrated energy storage power station, as well as the current operating status and future operating prediction status of the multi-functional integrated energy storage power station, the types of operating tasks in the current control cycle and future control cycles are identified, and then the operating task requirements are determined. Based on the available capacity, safety margin, and lifetime loss factor of each energy storage unit, the adaptability of each energy storage unit to the operational task requirements in the current control cycle and future control cycles is calculated, thereby adjusting the adaptability weight of each energy storage unit. Based on the adaptation weight, safety margin and lifetime loss factor of each energy storage unit, the operating parameters of each energy storage unit are generated, and multiple candidate power allocation schemes for the overall multi-element integrated energy storage power station are obtained. The candidate power allocation schemes are comprehensively constrained based on multiple physical quantities. The final power allocation scheme is determined based on the candidate power allocation schemes that pass the comprehensive constraint verification, so as to generate the control commands for the multi-element integrated energy storage power station.

2. The multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations according to claim 1, characterized in that, The operating physical quantities include terminal voltage, individual cell voltage, individual cell voltage difference, inter-cluster voltage difference, charging and discharging current, maximum temperature, minimum temperature, temperature difference, SOC, SOH, internal resistance, available capacity, rated power, maximum charging and discharging power, charging and discharging efficiency, cumulative cycle count, PCS response time, alarm status, and maintenance status. If the energy storage unit of the multi-element integrated energy storage power station includes a vanadium redox flow battery system, the corresponding operating physical quantities of the vanadium redox flow battery system also include electrolyte flow rate, pipeline pressure, electrolyte temperature, stack temperature, pump operating status, and stack power status.

3. The multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations according to claim 1, characterized in that, The preprocessing includes: filling in missing values, removing outliers, unifying units, aligning sampling times, and verifying boundaries for the collected physical quantities. The boundary verification includes: if the collected operating physical quantities exceed the corresponding preset reasonable range, then reduce the adaptation weight of the corresponding energy storage unit or remove the corresponding energy storage unit.

4. The multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations according to claim 1, characterized in that, The callable capabilities include rechargeable power, dischargeable power, available energy, continuous discharge duration, power ramp-up capability, and response time. The safety margins include SOC margin, temperature margin, voltage consistency margin, power margin, SOH margin, internal resistance margin, and fluid system pressure margin. The lifetime loss factor is calculated based on the depth of charge / discharge, charge / discharge rate, temperature level, number of cycles, SOH change rate, and internal resistance growth rate.

5. The multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations according to claim 1, characterized in that, The current operating status of the multi-functional integrated energy storage power station includes grid connection point power deviation, ancillary service demand, and market operation signals; The future operational forecast status includes load forecast results and new energy output forecast results; The indicators for the operational task types include long-term peak shaving, short-term peak shifting, rapid frequency regulation, backup support, renewable energy consumption, peak shaving and valley filling, grid connection point power smoothing, and market response. The operational task requirements include the requirements for power amplitude, energy capacity, response time, duration, and power ramp rate for each operational task type.

6. The multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations according to claim 1, characterized in that, The method calculates the adaptability of each energy storage unit to the operational task requirements based on the available power, available energy, response speed, continuous discharge duration, power ramp-up rate, safety margin, lifetime loss factor, and current SOC range of each energy storage unit. The operating parameters of the energy storage unit include target charge / discharge power, execution period, target SOC range, reserve capacity, and power ramp-up rate.

7. The multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations according to claim 1, characterized in that, The physical quantity constraints of the comprehensive constraint verification include: upper and lower limits of SOC, temperature limit, single-cell pressure difference limit, inter-cluster temperature difference limit, lower limit of SOH, internal resistance growth limit, fluid flow pressure limit, PCS power limit, grid connection point power limit, power ramp limit, and scheduling commitment constraints. In the process of comprehensive constraint verification, revenue evaluation factors, electricity price signals, ancillary service value and operating losses are also set as auxiliary evaluation factors to evaluate each candidate power allocation scheme and select the final power allocation scheme.

8. The multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations according to claim 1, characterized in that, The control commands for the multi-energy storage power station include: the charging and discharging status of each energy storage unit, target power, execution period, SOC target range, reserve capacity, power ramp-up rate, stop conditions, and protection threshold. The energy storage unit types of the multi-element integrated energy storage power station include lithium iron phosphate battery systems, semi-solid-state lithium battery systems, and vanadium redox flow battery systems.

9. The multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations according to claim 1, characterized in that, The method also includes continuously comparing the target power with the actual power, the predicted power with the actual power, the planned value of SOC with the actual value, the temperature change trend, the SOH change trend, the pressure difference change trend, and the power deviation at the grid connection point after the multi-energy storage power station executes the control command. If the deviation exceeds the corresponding deviation threshold, the callable capacity and adaptability of each energy storage unit are recalculated to correct subsequent control commands.

10. A multi-physical quantity constraint collaborative operation control system based on the multi-physical quantity constraint collaborative operation control method for multi-element integrated energy storage power stations as described in any one of claims 1-9, wherein the multi-element integrated energy storage power station includes multiple types of energy storage units, characterized in that, The system includes: The physical quantity acquisition module is used to acquire and preprocess the physical quantities of various types of energy storage units in the multi-electrode integrated energy storage power station. The callable capacity calculation module is used to calculate the callable capacity of each energy storage unit in the current control cycle based on the preprocessed operating physical quantities. The safety life assessment module is used to calculate the safety margin and life loss factor of each energy storage unit in the current control cycle based on the pre-processed operating physical quantities. The operation requirement identification module is used to identify the operation task type in the current control cycle and the future control cycle based on the grid dispatch instructions transmitted to the multi-electrode integrated energy storage power station, as well as the current operation status and future operation prediction status of the multi-electrode integrated energy storage power station, and then determine the operation task requirements. The multi-energy storage task adaptation module is used to calculate the adaptability of each energy storage unit to the operational task requirements in the current control cycle and future control cycles based on the available capacity, safety margin and life loss factor of each energy storage unit, thereby adjusting the adaptation weight of each energy storage unit. The collaborative optimization control module is used to generate the operating parameters of each energy storage unit based on the adaptation weight, safety margin and lifetime loss factor of each energy storage unit, and obtain multiple candidate power allocation schemes for the overall multi-element integrated energy storage power station. The strategy interface module is used to perform comprehensive constraint verification on each candidate power allocation scheme based on constraints of multiple physical quantities, and determine the final power allocation scheme based on the candidate power allocation schemes that pass the comprehensive constraint verification, so as to generate control commands for the multi-element integrated energy storage power station. The closed-loop feedback correction module is used to continuously compare the target power with the actual power, the predicted power with the actual power, the planned value of SOC with the actual value, the temperature change trend, the SOH change trend, the pressure difference change trend, and the power deviation at the grid connection point after the multi-energy storage power station executes the control command. If the deviation exceeds the corresponding deviation threshold, the available capacity and adaptability of each energy storage unit are recalculated to correct subsequent control commands.

Images