A multi-energy coordinated comprehensive energy storage power station intelligent management system

Abstract

The application discloses a kind of multi-energy synergistic comprehensive energy storage power station wisdom management systems, and relates to energy storage power station wisdom management technical field;The application acquires heterogeneous energy storage equipment operation and loss data by energy storage asset state perception module, constructs differentiated transient stress loss mechanism to calculate switching loss cost by switching stress loss quantification module, multi-objective synergistic scheduling function is included in switching loss cost by synergistic scheduling generation module, and multi-energy synergistic scheduling decision is generated by Thailand particle swarm optimization algorithm, switching execution and trajectory optimization module plans power transition trajectory and inhibits transient stress, and asset degradation closed-loop correction module carries out closed-loop correction to loss quantification mechanism based on actual response data;Effectively solve the problem of large switching loss in energy storage power station scheduling, asset health and scheduling benefit imbalance, significantly improve the economy of power station scheduling and the service life of energy storage asset, realize the dynamic balance of scheduling strategy and asset protection.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for energy storage power stations, specifically to a comprehensive intelligent management system for multi-energy collaborative energy storage power stations. Background Technology

[0002] The large-scale grid connection of renewable energy sources such as wind and solar has brought severe challenges to the stable operation and efficient absorption of the power grid due to their intermittency and volatility. Integrated energy storage power stations integrate heterogeneous energy storage units such as electrochemical, compressed air, and flywheel, and rely on multi-energy coordinated dispatch to achieve multiple functions such as power smoothing, peak-valley arbitrage, frequency regulation and voltage regulation. They have become the core carrier for improving the flexibility of the power grid and the utilization rate of new energy.

[0003] Current multi-energy collaborative scheduling systems mostly aim to optimize global economy, response speed, and regulation accuracy. They use intelligent algorithms to allocate the output of each energy storage unit in real time, pursuing the best global operation. Such systems generally treat scheduling strategy switching as a zero-cost event, and have not established a quantitative mapping model between strategy switching and the losses of heterogeneous energy storage devices. They cannot measure the transient electrical stress, thermal stress, and mechanical stress generated by behaviors such as switching of charging and discharging states, power changes, and operating condition conversions. Furthermore, they do not include the aging cost corresponding to a single switch in the optimization objectives. As the level of intelligent scheduling improves, the frequency of strategy switching increases significantly in order to adapt to source-load fluctuations. This causes heterogeneous energy storage units to operate under high-frequency, unsteady-state conditions for a long time, which exacerbates internal material fatigue, consistency degradation, and performance deterioration. This creates an implicit paradox: the more intelligent the scheduling, the more frequent the switching, and the faster the asset deterioration. At the same time, the existing management system lacks a collaborative closed loop between equipment health status and scheduling strategies, making it difficult to achieve a balance between global optimization and asset protection, which restricts the full life cycle benefits and safe and stable operation of energy storage power stations.

[0004] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0005] The purpose of this invention is to address the problems of high switching losses and imbalance between asset health and scheduling efficiency in energy storage power station scheduling, and to propose a multi-energy collaborative integrated intelligent management system for energy storage power stations.

[0006] The objective of this invention can be achieved through the following technical solutions: A multi-energy collaborative integrated intelligent management system for energy storage power stations includes: Energy storage asset status awareness module: acquires the operating status parameters and historical loss characteristic data of each heterogeneous energy storage device in the integrated energy storage power station through a distributed sensor network; Switching stress loss quantification module: Based on the acquired operating status parameters and historical loss characteristic data, a transient stress loss quantification mechanism for strategy switching behavior is constructed to obtain the loss cost corresponding to each switching behavior; Cooperative scheduling generation module: incorporates the cost of losses into the objective function of multi-energy cooperative scheduling to generate multi-energy cooperative scheduling decisions; Switching Execution and Trajectory Optimization Module: Based on the multi-energy collaborative scheduling decision output by the collaborative scheduling generation module, the execution strategy is switched and the transient stress impact during the switching process is reduced through dynamic trajectory planning; Asset deterioration closed-loop correction module: Based on the actual response data of the switching execution and trajectory optimization module, the loss quantification mechanism is corrected in a closed loop.

[0007] Furthermore, the specific operation steps of the energy storage asset status sensing module are as follows: The distributed sensor network is deployed in the electrochemical energy storage unit, compressed air energy storage unit and flywheel energy storage unit in the integrated energy storage power station to collect and upload the full-dimensional operation data of each heterogeneous energy storage device in real time. The distributed sensor network collects operating status parameters for electrochemical energy storage units, including power output, terminal voltage, and operating current; for compressed air energy storage units, it collects operating status parameters including power output, air tank pressure, and inlet and outlet temperatures; and for flywheel energy storage units, it collects operating status parameters including power output, rotor speed, and bearing temperature. Simultaneously, it retrieves historical loss characteristic datasets from historical databases for various energy storage devices, completes data acquisition and preprocessing based on a fixed sampling period, and standardizes and encapsulates the operating status parameters and historical loss characteristic data at the same moment.

[0008] Furthermore, the specific operation steps for switching the stress loss quantization module are as follows: Based on the operating status parameters and historical loss characteristics data of heterogeneous energy storage devices obtained by the energy storage asset status perception module, a differentiated transient stress loss quantification mechanism is constructed. For electrochemical energy storage units, the charge transition stress value is obtained based on the difference between the current state of charge and the target state of charge to be switched. For compressed air energy storage units, the pressure transition stress value is obtained based on the difference between the current air tank pressure and the target switching pressure. For the flywheel energy storage unit, the speed transition stress value is obtained based on the difference between the current speed and the target switching speed; The average interval of the last ten switching operations was statistically analyzed to obtain the switching frequency penalty factor. The transition stress value was normalized and combined with the equipment health status factor and the remaining life ratio to obtain the equipment vulnerability coefficient. For complex handover scenarios, based on normalized transition stress value, equipment vulnerability coefficient, handover frequency penalty factor and cooperative stress amplification factor, the comprehensive handover loss cost is obtained. The comprehensive handover loss cost is then converted into equivalent economic cost to obtain the handover economic loss cost corresponding to each handover behavior.

[0009] Furthermore, the specific operation steps of the collaborative scheduling generation module include: The power plant energy management platform collects load demand power, renewable energy output forecasts and grid dispatch instructions, and obtains the total regulation power target through power balance calculation. Combined with the operating status parameters and safety constraints of each heterogeneous energy storage device, the total target power is initially allocated among the energy storage units. A multi-objective collaborative scheduling function is constructed, which is composed of a weighted average of energy scheduling benefit term, switching loss penalty term, and equipment lifetime protection term. Based on the proportion of switching loss penalty term to energy scheduling benefit term, a dynamic switching threshold is obtained. Combined with the economic loss cost of switching, the expected scheduling benefit increment of strategy switching is obtained. Based on the expected increase in scheduling revenue, the dynamic switching threshold, and the economic cost of switching, combined with the switching execution judgment rules, the strategy switching execution decision is determined. The execution decision includes suppressing the current strategy switching and executing the current strategy switching.

[0010] Furthermore, the specific operation steps of the collaborative scheduling generation module also include: If the switching strategy execution decision is to execute this switching decision, the optimization objective is to maximize the total energy scheduling benefit within the scheduling period and minimize the switching loss and lifetime reduction penalty value. The dynamic switching threshold is used as a constraint condition. The particle swarm optimization algorithm is used to perform global optimization on the multi-objective cooperative scheduling function. All power allocation schemes and switching timing combinations within the scheduling period are traversed to select the optimal solution that maximizes the multi-objective cooperative scheduling function. The multi-energy collaborative scheduling decision is based on the optimal solution, including the target power command of various energy storage devices, the start time and execution sequence of strategy switching, the running time and power constraints of the entire scheduling cycle, and the power change rate limit of various energy storage devices during the switching process.

[0011] Furthermore, the specific operation steps of the switching execution and trajectory optimization module are as follows: Based on the multi-energy collaborative scheduling decision output by the collaborative scheduling generation module, an S-curve planning method is used to generate power transition trajectories for the energy storage devices participating in the switching, and the maximum allowable power change rate of each energy storage device is set based on the operating status of the electrochemical, compressed air, and flywheel energy storage units respectively. The actual power output value, internal temperature change rate and component stress index of the device are collected at a fixed sampling period. The power tracking deviation is obtained by comparing the actual power with the planned trajectory. When the power tracking deviation exceeds the preset allowable deviation threshold, the trajectory replanning mechanism is triggered and the remaining transition time is extended. Simultaneously, current pulsation, pressure fluctuation, and vibration monitoring links are deployed for various heterogeneous energy storage devices, and stress monitoring and suppression measures are implemented when each monitoring index exceeds the preset threshold. When multiple types of energy storage devices are switched synchronously, a timing-based peak-shaving control strategy is implemented. The switching timing is sorted based on the peak value of the grid-side inrush current, and the interval between the start-up times of adjacent devices is not less than 30% of the single device switching transition time.

[0012] Furthermore, the specific operation steps of the asset deterioration closed-loop correction module are as follows: After each switch is completed, the actual response data of each energy storage device is obtained, including the actual power transition time, peak stress index, temperature rise amplitude and energy conversion efficiency loss rate. The comprehensive switching loss cost is calculated based on the actual response data and compared with the comprehensive switching loss cost calculated in the switching stress loss quantification module to obtain the loss prediction deviation rate. If the loss prediction deviation rate exceeds the preset correction trigger threshold, the parameter correction process is initiated, the cause analysis is performed on the source of the deviation, and the correction direction and correction step size of the stress sensitivity coefficient, frequency penalty factor, and cooperative stress amplification factor are adjusted accordingly. Establish a mechanism to track equipment degradation trends, and calculate the cumulative switching loss value of each energy storage device at fixed intervals. When the cumulative switching loss value exceeds the equipment lifespan reduction warning threshold, increase the lifespan protection weight and dynamic switching threshold of the corresponding device. Based on the corrected parameters and the cumulative switching loss value of various energy storage devices, a periodic asset health report is generated and uploaded to the power plant energy management platform terminal. When the cumulative switching loss value reaches the preset maintenance recommendation threshold, an equipment maintenance warning is triggered.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention deploys a distributed sensor network to perceive the full-dimensional operation and loss data of heterogeneous energy storage devices, constructs a differentiated transient stress loss quantification mechanism to accurately calculate switching loss costs, incorporates losses into a multi-objective scheduling function to generate scheduling decisions that balance energy optimization and asset protection, plans an S-shaped power trajectory and implements transient stress suppression, and simultaneously corrects loss quantification parameters based on actual switching response data in a closed loop, tracks equipment degradation trends, and dynamically adjusts scheduling strategies. This significantly improves the scientific and economic efficiency of multi-energy collaborative scheduling in energy storage power stations, effectively reduces transient stress impacts and asset losses during equipment strategy switching, extends the actual service life of energy storage devices, achieves a dynamic balance between power station scheduling benefits and energy storage asset protection, and enhances the overall stability and intelligence level of power station operation. Attached Figure Description

[0014] Figure 1 This is the overall system block diagram of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example: like Figure 1 As shown, a multi-energy collaborative integrated intelligent management system for energy storage power stations includes an energy storage asset status perception module, a switching stress loss quantification module, a collaborative scheduling generation module, a switching execution and trajectory optimization module, and an asset deterioration closed-loop correction module.

[0017] Energy storage asset status awareness module: This module acquires the operating status parameters and historical loss characteristics of various heterogeneous energy storage devices in the integrated energy storage power station through a distributed sensor network. The specific implementation process is as follows: The distributed sensor network is deployed in various heterogeneous energy storage devices within the integrated energy storage power station, including electrochemical energy storage units, compressed air energy storage units, and flywheel energy storage units. The distributed sensor network adopts a multi-node, full-coverage deployment method to collect and upload full-dimensional operational data of each heterogeneous energy storage device in real time. For electrochemical energy storage units, voltage sensors, current sensors, temperature sensors, and a battery management system are used to collect current power output values, terminal voltage, operating current, state of charge (SOC), internal temperatures of individual cells and modules, inter-cluster temperature difference, current cumulative operating time, current cycle count, internal resistance change rate, capacity decay rate, and charge / discharge efficiency, forming a set of operating status parameters for the electrochemical energy storage unit. Simultaneously, historical databases are retrieved to extract historical switching counts, historical switching interval duration sequences, historical power change amplitude sequences before and after historical switching, historical state of charge transition amplitudes, and corresponding loss data for the current electrochemical energy storage unit, forming a historical loss characteristic dataset for the electrochemical energy storage unit. For compressed air energy storage units, pressure sensors, temperature sensors, flow sensors, and air compressor and expander controllers are used to collect current power output values, actual pressure of the air tank, intake and exhaust temperatures, unit operating speed, cumulative operating time, and unit efficiency to form a set of operating status parameters for the compressed air energy storage unit. Simultaneously, historical databases are retrieved to extract the historical switching count, historical switching interval sequence, pressure change amplitude before and after historical switching, power change amplitude, and corresponding loss data for the current compressed air energy storage unit, forming a historical loss feature dataset for the compressed air energy storage unit. For the flywheel energy storage unit, current power output value, rotor speed, bearing temperature, housing vibration amplitude, cumulative operating time, number of start-stop cycles, and charging / discharging efficiency are collected through speed sensors, vibration sensors, temperature sensors, and the flywheel controller to form a set of operating status parameters for the flywheel energy storage unit. Simultaneously, the historical database is retrieved to extract the historical number of start-stop cycles, historical switching interval duration sequence, historical speed change amplitude before and after switching, historical vibration over-limit records, and corresponding loss data for the current flywheel energy storage unit, forming a historical loss feature dataset for the flywheel energy storage unit. Distributed sensor networks complete data acquisition and preprocessing with a fixed sampling period, remove outliers, perform data filtering and timestamp alignment, and standardize and encapsulate the operating status parameters of each heterogeneous energy storage device at the same time with the corresponding historical loss characteristic data.

[0018] Switching stress loss quantification module: Based on the acquired operating status parameters and historical loss characteristic data, a transient stress loss quantification mechanism for strategy switching behavior is constructed to obtain the loss cost corresponding to each switching behavior. The specific implementation process is as follows: Based on the acquired operating status parameters and historical loss characteristics data of various heterogeneous energy storage devices, a differentiated transient stress loss quantification mechanism is constructed for electrochemical energy storage unit, compressed air energy storage unit and flywheel energy storage unit respectively, to quantify the state transition stress generated by the equipment during a single scheduling strategy switch. The current power output value, state of charge, internal temperature, cumulative operating time, and rated cycle life of each heterogeneous energy storage device are extracted from the operating status parameters. Simultaneously, historical switching frequency, historical switching interval duration, and power change amplitude sequences before and after historical switching are extracted from historical loss characteristic data. State transition stress calculations are then performed for each type of energy storage device. For electrochemical energy storage units, based on the difference between the current state of charge and the target state of charge, the formula is used... The charge transition stress value was calculated. ,in, This indicates the current state of charge of the electrochemical energy storage unit, that is, the percentage of the remaining battery capacity to the rated capacity. This indicates that the electrochemical energy storage unit is switching to the target state of charge. The stress sensitivity coefficient of the electrochemical energy storage unit is obtained by looking up a preset parameter lookup table based on the battery type and the current internal temperature of the device. For compressed air energy storage units, based on the current air tank pressure With switching target pressure The difference is expressed by the formula. The stress value of pressure transition was calculated. ,in, The stress sensitivity coefficient of the compressed air energy storage unit is a preset fixed parameter. Indicates the temperature compensation factor. This indicates the deviation between the current internal temperature of the compressed air energy storage unit and the standard operating temperature. For flywheel energy storage units, based on the current rotational speed With switching target speed The difference is expressed by the formula. The stress value of the rotational speed transition was calculated. ,in, This represents the stress sensitivity coefficient of the flywheel energy storage unit. Indicates the bearing wear accumulation factor. This indicates the cumulative number of start-ups and shutdowns of the flywheel energy storage unit throughout history. Based on the historical handover interval duration sequence, the average interval duration of the last ten handovers was calculated. Through formula The switching frequency penalty factor is calculated, where, , , representing the frequency attenuation constant, Represents an exponential function; The transition stress values ​​corresponding to each heterogeneous energy storage device are normalized to obtain normalized transition stress values. The vulnerability coefficient of each heterogeneous energy storage device is obtained by multiplying its current health status factor and remaining lifespan ratio by the reciprocal. The health status factor is based on the current internal resistance change rate and capacity decay rate of the energy storage device. The remaining lifespan ratio refers to the ratio of the remaining number of cycles of the current energy storage device to its rated cycle life. For complex switching scenarios involving multiple types of energy storage devices, based on the normalized transition stress values, device vulnerability coefficients, and switching frequency penalty factors of each heterogeneous energy storage device, a formula is used to... The comprehensive switching loss cost is calculated. ,in, This indicates the number of energy storage device types participating in this switchover. Indicates the first Normalized transition stress value of energy storage devices Indicates the first Equipment vulnerability coefficient of energy storage devices This represents the collaborative stress amplification factor for multi-device coupling switching; The synergistic stress amplification factor is calculated using the following formula:

[0019] in, The impact superposition coefficient for synchronous operation is a preset constant. Indicates the number of devices that start or stop simultaneously during this switchover; The comprehensive switching loss value is converted into equivalent economic cost through the formula. Obtain the switching economic loss cost corresponding to this switching action. ,in, Indicates the first The unit capacity replacement cost of energy storage devices Indicates the first Rated capacity of energy storage devices Indicates the first Rated cycle life of energy storage devices.

[0020] Cooperative scheduling generation module: This module incorporates the cost of energy loss into the objective function of multi-energy cooperative scheduling, generating cooperative scheduling decisions that balance global energy optimization and asset health protection. The specific implementation process is as follows: The power plant energy management platform collects and obtains the current load demand power, renewable energy output forecast, and grid dispatch instructions in real time, and obtains the total regulation power target based on the power balance relationship. Load demand includes constant base load and fluctuating adjustable load; renewable energy output forecasts are obtained by collecting historical meteorological data and real-time meteorological monitoring data, including irradiance, wind speed, wind direction, ambient temperature, air pressure and precipitation probability, combined with the rated parameters, operating efficiency characteristics and historical power generation curves of renewable energy generator units, using time-series forecasting methods to obtain the power generation forecasts for each time period in the future dispatch cycle; grid dispatch instructions include peak shaving and frequency regulation instructions; Based on the total regulation power target, and combined with the operating status parameters and safety constraints of each heterogeneous energy storage device, the total target power is initially allocated among the electrochemical energy storage unit, compressed air energy storage unit, and flywheel energy storage unit to obtain the initial target power allocation value for each type of energy storage unit; the safety constraints include the current health status of the equipment, the maximum allowable output, and the minimum operating limit; A multi-objective cooperative scheduling function is constructed, which includes switching loss costs. This function is composed of a weighted average of energy scheduling benefit terms, switching loss penalty terms, and equipment lifetime protection terms, where: The energy dispatch benefit term is calculated using the following formula:

[0021] in, This represents the total energy scheduling revenue within the scheduling period. Indicates the number of time periods within the scheduling period. Indicates the first Energy storage devices in the first Power output during a given time period Indicates the first The charging and discharging efficiency of energy storage devices. Indicates the first Real-time electricity price for a given time period; The switching loss penalty is calculated using the following formula:

[0022] in, This represents the total switching loss penalty value within the scheduling period. Indicates the first Energy storage devices in the first The economic cost of switching when a time period changes. Indicates the first Energy storage devices in the first and The value is 1 when a strategy switch occurs during a time period, and 0 otherwise. Equipment life protection items are calculated using the following formula:

[0023] in, This indicates the penalty value for reduced equipment lifespan. Indicates the first Lifetime protection weights for energy storage devices Indicates the first The remaining lifespan percentage of energy storage devices Indicates the first The average power output of energy storage devices during the dispatch cycle; Based on the proportion of switching loss penalty to energy scheduling benefit, using the formula... The dynamic switching threshold is calculated, where, Indicates the baseline switching threshold. This represents the threshold adjustment sensitivity coefficient; Based on the dynamic switching threshold and the economic loss cost corresponding to this switching, the expected scheduling revenue increment for executing this strategy switching is obtained; the expected scheduling revenue increment refers to the difference between the total energy scheduling revenue of the integrated energy storage power station after the switching and the total energy scheduling revenue of maintaining the current operating state within the scheduling cycle. Based on the expected increase in scheduling revenue, the dynamic handover threshold, and the economic cost of handover, the following handover execution determination rules are implemented: If the expected increase in scheduling revenue is less than the product of the dynamic switching threshold and the economic cost of switching, then the current strategy switch will be suppressed and the current operating state will be maintained. If the expected increase in scheduling revenue is greater than the product of the dynamic switching threshold and the economic cost of switching, then this strategy switch will be executed. Based on the premise that the switching execution judgment rule determines that the strategy switching should be executed, the optimization objectives are to maximize the total energy scheduling benefit and minimize the switching loss and lifetime reduction penalty within the scheduling period. With the dynamic switching threshold as the constraint, the particle swarm optimization algorithm is used to perform global optimization of the multi-objective cooperative scheduling function. All feasible power allocation schemes and switching timing combinations within the scheduling period are traversed to select the optimal solution that maximizes the multi-objective cooperative scheduling function. Based on the optimal solution obtained through global optimization, the multi-energy cooperative scheduling decision is output, including: Target power commands for electrochemical energy storage units, compressed air energy storage units, and flywheel energy storage units; The start time and execution sequence of strategy switching for various energy storage devices; The operating time and power constraints of each energy storage device throughout the entire scheduling cycle; During strategy switching, various energy storage devices are allowed to follow the power change rate limits.

[0024] Switching Execution and Trajectory Optimization Module: Based on the multi-energy collaborative scheduling decision output by the collaborative scheduling generation module, the execution strategy is switched, and the transient stress impact during the switching process is reduced through dynamic trajectory planning. The specific implementation process is as follows: Based on the target power command, strategy switching initiation time, and execution sequence output from the multi-energy collaborative scheduling decision, a power transition trajectory is generated for each type of energy storage device participating in the switching. The power transition trajectory uses an S-curve planning method, dividing the power switching process into seven stages: acceleration, uniform acceleration, gradual acceleration, uniform speed, acceleration / deceleration, uniform deceleration, and deceleration. This is achieved through formulas... Calculations are performed to obtain any time. The actual output power of the energy storage device ,in, This represents the initial baseline value before the energy storage device switches its strategy. This represents the acceleration function of power change, characterizing the real-time rate of change of the power change rate of the energy storage device during power switching. This represents the change in power from the start-up point to any other time. This represents the differential time variable in calculus; For electrochemical energy storage units, the maximum allowable rate of change of power is determined based on the current state of charge range. When the state of charge is within the preset safe range, the maximum permissible rate of change of power is taken as the rated value; when the state of charge is close to the upper or lower limit, the maximum permissible rate of change of power is determined by the formula. To reduce the maximum permissible power change rate of electrochemical energy storage units, among which, This represents the rate of change of the rated power of an electrochemical energy storage unit. Represents the boundary sensitivity coefficient. This indicates the distance of the currently charged body from the boundary; For compressed air energy storage units, the power ramp-up constraint of the expander or compressor is obtained based on the ratio of the current pressure to the rated pressure of the air tank, using the formula... Obtain the maximum permissible power change rate of the compressed air energy storage unit. ,in, This indicates the rate of change of the rated power of the compressed air energy storage unit. This indicates the rated pressure of the compressed air storage tank in the compressed air energy storage unit; For flywheel energy storage units, based on the ratio of the current speed to the rated speed, a power rise and fall constraint is obtained, which reduces the rate of power change in the high speed range and suppresses the vibration and impact of the rotor and bearing components. During the power transition process, the actual power output value, internal temperature change rate, and component stress index of each energy storage device are obtained at a fixed sampling period. The actual power output value is compared with the planned trajectory to obtain the power tracking deviation. The stress index refers to the magnitude and change state of the stress borne by the functional components of each heterogeneous energy storage device in the integrated energy storage power station during strategy switching and power transition. When the absolute value of the power tracking deviation exceeds the preset allowable deviation threshold, it is determined to be an abnormal trajectory execution, triggering the trajectory replanning mechanism. The current actual power value is used as the new starting point to regenerate the transition trajectory to reach the target power, and the remaining transition time is extended to 1.5 times the original planned time. Implement transient stress monitoring and suppression measures during power transition: For the deployment of current pulsation detection in electrochemical energy storage units, the amplitude of high-frequency pulsation components is extracted by performing a fast Fourier transform on the sampled current signal. When the amplitude of the high-frequency pulsation component exceeds a preset pulsation threshold, the power change acceleration is reduced to 50% of its original value. For the compressed air energy storage unit, a pressure fluctuation detection link is deployed. By calculating the variance of the pressure signal of the air storage tank through a sliding window, the pressure fluctuation index is obtained. When the pressure fluctuation index exceeds the preset fluctuation threshold, the power regulation operation is paused and the current power output is maintained until the pressure fluctuation index drops below the fluctuation threshold. For the flywheel energy storage unit, a vibration monitoring system is deployed. Vibration signals are acquired by an accelerometer installed on the bearing housing, and the root mean square value of the vibration signal is extracted. When the root mean square value exceeds a preset threshold, protective deceleration is performed at a rate of 1% reduction in rotational speed per second. When multiple types of energy storage devices perform switching operations simultaneously, a timing-based peak-shaving control strategy is implemented. The switching operations of the devices with the largest peak inrush current generated during the switching process are sorted, and the switching operation of the device with the largest peak inrush current is placed first in the timing sequence. The start time interval between the switching operations of two adjacent types of devices is not less than 30% of the single device switching transition time, so as to avoid the voltage dip on the grid side caused by the superposition of inrush currents.

[0025] Asset Deterioration Closed-Loop Correction Module: Based on the actual response data from the switchover execution and trajectory optimization modules, the loss quantification mechanism is corrected in a closed loop to achieve a dynamic balance between scheduling strategies and asset protection. The specific implementation process is as follows: After each switch is completed, the actual response data of each energy storage device during the switch is obtained, including the actual power transition time, peak stress index, temperature rise amplitude and energy conversion efficiency loss rate. The actual response data is compared with the comprehensive switching loss cost calculated during the switching stress loss quantification stage, using the formula... The loss prediction deviation rate is calculated, where, This represents the comprehensive switching loss cost calculated based on actual response data. This represents the estimated total switching loss cost, which is the total switching loss cost calculated in the switching stress loss quantification module. When the loss prediction deviation rate exceeds the preset correction trigger threshold, the parameter correction process is initiated to perform attribution analysis on the source of the deviation, specifically including: Based on the sign and magnitude of the difference between the actual transition stress value and the transition stress value calculated in the switching stress loss quantification module, the correction direction and correction step size of the stress sensitivity coefficient are adjusted accordingly. Based on the sign and magnitude of the difference between the actual switching interval and the statistical average interval, the correction direction and correction step size of the frequency penalty factor are adjusted accordingly. Compare the sign and magnitude of the difference between the actual synergistic impact effect and the synergistic stress amplification effect, and adjust the correction direction and correction step size of the synergistic stress amplification factor accordingly. Establish a mechanism to track equipment degradation trends, and periodically calculate the cumulative switching loss value of each energy storage device using a formula. Calculations are performed to obtain the first Cumulative switching loss value of energy storage devices ,in, This refers to the number of switches within the statistical period. Indicates the first Energy storage devices in the statistical period The actual overall switching loss value corresponding to each strategy switch; When the cumulative switching loss exceeds the equipment lifespan reduction warning threshold, the lifespan protection weight of the current energy storage equipment will be increased to 1.5 times the original value in subsequent collaborative scheduling decisions, and the dynamic switching threshold of the current energy storage equipment will be increased to 2 times the original value. The revised parameters and the cumulative switching loss value of various energy storage devices are used to generate periodic asset health reports; these reports are simultaneously uploaded to the power plant energy management platform terminal, and equipment maintenance warnings are triggered when the cumulative switching loss value reaches the preset maintenance recommendation threshold.

[0026] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A comprehensive intelligent management system for multi-energy collaborative energy storage power stations, characterized in that, include: Energy storage asset status awareness module: acquires the operating status parameters and historical loss characteristic data of each heterogeneous energy storage device in the integrated energy storage power station through a distributed sensor network; Switching stress loss quantification module: Based on the acquired operating status parameters and historical loss characteristic data, a transient stress loss quantification mechanism for strategy switching behavior is constructed to obtain the loss cost corresponding to each switching behavior; Cooperative scheduling generation module: incorporates the cost of losses into the objective function of multi-energy cooperative scheduling to generate multi-energy cooperative scheduling decisions; Switching Execution and Trajectory Optimization Module: Based on the multi-energy collaborative scheduling decision output by the collaborative scheduling generation module, the execution strategy is switched and the transient stress impact during the switching process is reduced through dynamic trajectory planning; Asset deterioration closed-loop correction module: Based on the actual response data of the switching execution and trajectory optimization module, the loss quantification mechanism is corrected in a closed loop.

2. The intelligent management system for a multi-energy collaborative integrated energy storage power station according to claim 1, characterized in that, The specific operation steps of the energy storage asset status sensing module are as follows: The distributed sensor network is deployed in the electrochemical energy storage unit, compressed air energy storage unit and flywheel energy storage unit in the integrated energy storage power station to collect and upload the full-dimensional operation data of each heterogeneous energy storage device in real time. The distributed sensor network collects operating status parameters for electrochemical energy storage units, including power output, terminal voltage, and operating current; for compressed air energy storage units, it collects operating status parameters including power output, air tank pressure, and inlet and outlet temperatures; and for flywheel energy storage units, it collects operating status parameters including power output, rotor speed, and bearing temperature. Simultaneously, it retrieves historical loss characteristic datasets from historical databases for various energy storage devices, completes data acquisition and preprocessing based on a fixed sampling period, and standardizes and encapsulates the operating status parameters and historical loss characteristic data at the same moment.

3. The intelligent management system for a multi-energy collaborative integrated energy storage power station according to claim 1, characterized in that, The specific operation steps for switching the stress loss quantization module are as follows: Based on the operating status parameters and historical loss characteristics data of heterogeneous energy storage devices obtained by the energy storage asset status perception module, a differentiated transient stress loss quantification mechanism is constructed. For electrochemical energy storage units, the charge transition stress value is obtained based on the difference between the current state of charge and the target state of charge to be switched. For compressed air energy storage units, the pressure transition stress value is obtained based on the difference between the current air tank pressure and the target switching pressure. For the flywheel energy storage unit, the speed transition stress value is obtained based on the difference between the current speed and the target switching speed; The average interval of the last ten switching operations was statistically analyzed to obtain the switching frequency penalty factor. The transition stress value was normalized and combined with the equipment health status factor and the remaining life ratio to obtain the equipment vulnerability coefficient. For complex handover scenarios, based on normalized transition stress value, equipment vulnerability coefficient, handover frequency penalty factor and cooperative stress amplification factor, the comprehensive handover loss cost is obtained. The comprehensive handover loss cost is then converted into equivalent economic cost to obtain the handover economic loss cost corresponding to each handover behavior.

4. The intelligent management system for a multi-energy collaborative integrated energy storage power station according to claim 1, characterized in that, The specific operation steps of the collaborative scheduling generation module include: The power plant energy management platform collects load demand power, renewable energy output forecasts and grid dispatch instructions, and obtains the total regulation power target through power balance calculation. Combined with the operating status parameters and safety constraints of each heterogeneous energy storage device, the total target power is initially allocated among the energy storage units. A multi-objective collaborative scheduling function is constructed, which is composed of a weighted average of energy scheduling benefit term, switching loss penalty term, and equipment lifetime protection term. Based on the proportion of switching loss penalty term to energy scheduling benefit term, a dynamic switching threshold is obtained. Combined with the economic loss cost of switching, the expected scheduling benefit increment of strategy switching is obtained. Based on the expected increase in scheduling revenue, the dynamic switching threshold, and the economic cost of switching, combined with the switching execution judgment rules, the strategy switching execution decision is determined. The execution decision includes suppressing the current strategy switching and executing the current strategy switching.

5. The intelligent management system for a multi-energy collaborative integrated energy storage power station according to claim 1, characterized in that, The specific operation steps of the cooperative scheduling generation module also include: If the switching strategy execution decision is to execute this switching decision, the optimization objective is to maximize the total energy scheduling benefit within the scheduling period and minimize the switching loss and lifetime reduction penalty value. The dynamic switching threshold is used as a constraint condition. The particle swarm optimization algorithm is used to perform global optimization on the multi-objective cooperative scheduling function. All power allocation schemes and switching timing combinations within the scheduling period are traversed to select the optimal solution that maximizes the multi-objective cooperative scheduling function. The multi-energy collaborative scheduling decision is based on the optimal solution, including the target power command of various energy storage devices, the start time and execution sequence of strategy switching, the running time and power constraints of the entire scheduling cycle, and the power change rate limit of various energy storage devices during the switching process.

6. The intelligent management system for a multi-energy collaborative integrated energy storage power station according to claim 1, characterized in that, The specific operation steps of the switching execution and trajectory optimization module are as follows: Based on the multi-energy collaborative scheduling decision output by the collaborative scheduling generation module, an S-curve planning method is used to generate power transition trajectories for the energy storage devices participating in the switching, and the maximum allowable power change rate of each energy storage device is set based on the operating status of the electrochemical, compressed air, and flywheel energy storage units respectively. The actual power output value, internal temperature change rate and component stress index of the device are collected at a fixed sampling period. The power tracking deviation is obtained by comparing the actual power with the planned trajectory. When the power tracking deviation exceeds the preset allowable deviation threshold, the trajectory replanning mechanism is triggered and the remaining transition time is extended. Simultaneously, current pulsation, pressure fluctuation, and vibration monitoring links are deployed for various heterogeneous energy storage devices, and stress monitoring and suppression measures are implemented when each monitoring index exceeds the preset threshold. When multiple types of energy storage devices are switched synchronously, a timing-based peak-shaving control strategy is implemented. The switching timing is sorted based on the peak value of the grid-side inrush current, and the interval between the start-up times of adjacent devices is not less than 30% of the single device switching transition time.

7. The intelligent management system for a multi-energy collaborative integrated energy storage power station according to claim 1, characterized in that, The specific operation steps of the asset deterioration closed-loop correction module are as follows: After each switch is completed, the actual response data of each energy storage device is obtained, including the actual power transition time, peak stress index, temperature rise amplitude and energy conversion efficiency loss rate. The comprehensive switching loss cost is calculated based on the actual response data and compared with the comprehensive switching loss cost calculated in the switching stress loss quantification module to obtain the loss prediction deviation rate. If the loss prediction deviation rate exceeds the preset correction trigger threshold, the parameter correction process is initiated, the cause analysis is performed on the source of the deviation, and the correction direction and correction step size of the stress sensitivity coefficient, frequency penalty factor, and cooperative stress amplification factor are adjusted accordingly. Establish a mechanism to track equipment degradation trends, and calculate the cumulative switching loss value of each energy storage device at fixed intervals. When the cumulative switching loss value exceeds the equipment lifespan reduction warning threshold, increase the lifespan protection weight and dynamic switching threshold of the corresponding device. Based on the corrected parameters and the cumulative switching loss value of various energy storage devices, a periodic asset health report is generated and uploaded to the power plant energy management platform terminal. When the cumulative switching loss value reaches the preset maintenance recommendation threshold, an equipment maintenance warning is triggered.

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