Intelligent electric quantity scheduling method, system and equipment for energy storage

Abstract

The invention discloses an intelligent electric quantity dispatching method, system and equipment for energy storage, and relates to the technical field of electric quantity dispatching. Dynamically calibrating key parameters of a profit measuring and calculating model based on the energy storage system operation data; performing energy storage income measurement and calculation through the calibrated income budgeting model to obtain an energy storage measurement and calculation result; and performing operation parameter optimization feedback on the operation data of the energy storage system according to the energy storage measurement and calculation result, and updating an energy storage scheduling strategy. According to the invention, the technical problem of low operation efficiency and income of the energy storage system caused by inaccurate energy storage income measurement and calculation and insufficient scheduling strategy optimization in the prior art is solved, and the technical effects of accurate energy storage income measurement and calculation, scheduling strategy optimization and improvement of the operation efficiency and income of the energy storage system are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to an intelligent power dispatching method, system and device for energy storage. Background Art

[0002] In the energy storage field, existing power scheduling methods often have the problem that the parameters of the profit calculation model cannot be dynamically calibrated according to the real-time operating data of the energy storage system, resulting in inaccurate profit calculation. At the same time, the scheduling strategy optimization process lacks comprehensive consideration of real-time constraints and predictive constraints, making it difficult to effectively optimize the operating parameters, resulting in low operating efficiency of the energy storage system and failure to maximize profits.

[0003] Existing technologies have technical problems such as inaccurate calculation of energy storage benefits and insufficient optimization of scheduling strategies, which lead to low operating efficiency and benefits of energy storage systems. Summary of the Invention

[0004] This application provides an intelligent power scheduling method, system and equipment for energy storage, which is used to solve the technical problems in the existing technology such as inaccurate energy storage benefit calculation and insufficient optimization of scheduling strategies, resulting in low operating efficiency and benefits of energy storage systems.

[0005] In view of the above problems, the present application provides an intelligent power scheduling method, system and device for energy storage.

[0006] In a first aspect of the present application, a smart power scheduling method for energy storage is provided, the method comprising:

[0007] Collect energy storage system operating data, including charge and discharge depth, operating rate, temperature, number of cycles, battery status, and SOC-OCV curve; dynamically calibrate key parameters of the benefit calculation model based on the energy storage system operating data; calculate energy storage benefits using the calibrated benefit budget model to obtain energy storage calculation results; and perform operating parameter optimization feedback on the energy storage system operating data based on the energy storage calculation results to update the energy storage scheduling strategy.

[0008] A second aspect of the present application provides an intelligent power dispatching system for energy storage, the system comprising:

[0009] An operating data acquisition module is used to collect operating data of the energy storage system, including charge and discharge depth, operating rate, temperature, number of cycles, battery status, and SOC-OCV curve; a key parameter calibration module is used to dynamically calibrate key parameters of the benefit calculation model based on the operating data of the energy storage system; an energy storage calculation result acquisition module is used to calculate energy storage benefits through the calibrated benefit budget model to obtain energy storage calculation results; and an energy storage scheduling strategy update module is used to perform operating parameter optimization feedback on the operating data of the energy storage system based on the energy storage calculation results and update the energy storage scheduling strategy.

[0010] The third aspect of the present application provides an electronic device, which includes: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute the intelligent power scheduling method for energy storage provided in the present application.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] The system collects energy storage system operating data, including charge and discharge depth, operating rate, temperature, number of cycles, battery status, and SOC-OCV curves. Based on this energy storage system operating data, it dynamically calibrates key parameters of the benefit calculation model. Energy storage benefits are calculated using the calibrated benefit budget model to obtain energy storage calculation results. Based on this energy storage calculation result, the system performs operating parameter optimization feedback on the energy storage system operating data and updates the energy storage scheduling strategy. This achieves accurate energy storage benefit calculation and scheduling strategy optimization, improving the operating efficiency and benefits of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A flow chart of an intelligent power scheduling method for energy storage provided in an embodiment of the present application.

[0015] Figure 2 A schematic diagram of the structure of an intelligent power dispatching system for energy storage provided in an embodiment of the present application.

[0016] Figure 3 This is a schematic diagram of the structure of an electronic device provided in this application.

[0017] Explanation of the reference numerals: operating data acquisition module 10 , key parameter calibration module 20 , energy storage measurement result acquisition module 30 , energy storage scheduling strategy update module 40 , processor 21 , memory 22 , input device 23 , output device 24 . DETAILED DESCRIPTION

[0018] This application provides an intelligent power scheduling method, system and equipment for energy storage, which is used to solve the technical problems in the existing technology such as inaccurate energy storage benefit calculation and insufficient optimization of scheduling strategies, resulting in low operating efficiency and benefits of energy storage systems.

[0019] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0020] Example 1, as Figure 1 As shown, the present application provides an intelligent power scheduling method for energy storage, the method comprising:

[0021] Step S100: Collecting energy storage system operating data, including charge and discharge depth, operating rate, temperature, cycle number, battery status, and SOC-OCV curve.

[0022] Specifically, energy storage system operating data is collected, including charge and discharge depth, operating rate, temperature, number of cycles, battery state, and SOC-OCV curve. The charge and discharge depth reflects the battery's energy utilization, the operating rate reflects the ratio of charge and discharge power to rated power, temperature parameters affect battery performance and safety, the number of cycles assesses battery life loss, the battery state reflects its health, and the SOC-OCV curve shows the relationship between state of charge and open circuit voltage. This data collection provides basic data support for the subsequent dynamic calibration of key parameters of the benefit calculation model, energy storage benefit calculation, and updating of energy storage scheduling strategies.

[0023] In one possible implementation, step S100 further includes:

[0024] Step S110: Acquire status data of the energy storage device and energy storage power demand.

[0025] Step S120: Decompose the energy storage power requirement according to the status data of the energy storage device, search for the charge and discharge power and time window of each energy storage device with the goal of minimizing the loss of energy storage device and maximizing the energy storage power requirement, and control the operating status of each energy storage device in the energy storage system.

[0026] Specifically, the status data of energy storage equipment is obtained in real time through sensors, communication interfaces, etc., including parameters that characterize the equipment's operating performance, such as equipment health status, remaining capacity, and charge and discharge efficiency. At the same time, the energy storage demand of users or the grid side is obtained, and the target amount of energy to be stored or released is clarified, providing basic data support for subsequent energy decomposition and operating status control based on equipment status.

[0027] A mixed integer linear programming (MILP) algorithm combined with a model predictive control (MPC) strategy is adopted. The objective functions are to minimize the loss of energy storage equipment (establish a decay cost function that includes the effects of charge and discharge depth, number of cycles, and temperature) and maximize the satisfaction of energy storage demand (set a demand power deviation threshold constraint). The energy storage device status data (remaining capacity, health status index, charge and discharge efficiency curve) is used as a constraint condition. The rolling optimization window is divided in the time dimension. The branch and bound method is used to search for the optimal charge and discharge power integer solution (satisfying the device rate limit) and the continuous time window boundary of each device in each time window. At the same time, a penalty function is introduced to handle the power decomposition deviation. Finally, the charge and discharge power sequence and time window control instructions of each device are generated to achieve global optimization of power decomposition and equipment loss.

[0028] Step S200: Dynamically calibrating key parameters of a benefit calculation model based on the energy storage system operation data.

[0029] Specifically, the energy storage system operating data, including charge and discharge depth, operating rate, temperature, number of cycles, battery status, SOC-OCV curve, etc., is collected to provide data support for the establishment and calibration of the benefit calculation model. Then, a dynamic calibration factor library is established: the influencing parameters of the efficiency factor, capacity factor, and attenuation factor are analyzed, and the response influence relationship between each factor and the influencing parameter is established. The parameter monitoring values ​​are collected in real time through sensors and other equipment. When the value change exceeds the preset threshold, the factor value is dynamically configured based on the response influence relationship and the factor library is updated. The factors in the dynamic calibration factor library are then used to update and calibrate the benefit budget model: the efficiency factor is associated with the charge and discharge efficiency calculation, the capacity factor is associated with the battery available capacity assessment, and the attenuation factor is associated with the battery life loss cost calculation. Combined with parameters such as the peak-valley electricity price difference and the charge and discharge volume, a mathematical model with the goal of maximizing net profit is constructed (for example, the objective function is the charge and discharge price difference profit within the scheduling period minus the battery attenuation cost), thereby completing the establishment of the benefit calculation model. Finally, based on the real-time collected energy storage system operation data, key parameters such as efficiency, capacity, and attenuation in the model are continuously updated through the dynamic calibration factor library to achieve dynamic calibration of the profit calculation model, ensuring that the model accurately reflects the profit situation under the actual operating status of the energy storage system.

[0030] In one possible implementation, step S200 further includes:

[0031] Step S210: establishing a dynamic calibration factor library, including efficiency factor, capacity factor, and attenuation factor.

[0032] Step S220: Utilizing the dynamic calibration factor library to update and calibrate the evaluation factors of the revenue budget model.

[0033] Step S230: Utilizing the updated and calibrated benefit budget model, the energy storage benefit is calculated based on the energy storage system operation data.

[0034] Specifically, a dynamic calibration factor library is established. For the efficiency factor, an in-depth analysis is conducted on the effects of temperature (T), operating rate (C-rate), and depth of charge / discharge (DOD) on battery charge / discharge efficiency, collecting efficiency data under different combinations of conditions. For the capacity factor, the impact of factors such as battery health (SOH), operating rate (C-rate), and temperature (T) on the battery's available capacity is considered to determine the relevant capacity change values. For the attenuation factor, the effects of parameters such as the number of cycles (N), intraday temperature difference (ΔT), and average depth of charge / discharge (DOD_avg) on ​​battery life are studied to quantify the corresponding attenuation ratio. By integrating this data, a dynamic calibration factor library containing efficiency factor, capacity factor, and attenuation factor is constructed, providing key data support for subsequent precise analysis and optimized scheduling of energy storage systems.

[0035] The evaluation factors of the benefit budget model are updated and calibrated using a dynamic calibration factor library. First, data such as the efficiency factor, capacity factor, and attenuation factor from the dynamic calibration factor library are imported into the benefit budget model using a data interface. For the efficiency factor, real-time temperature and charge / discharge rate data are obtained through temperature sensors and charge / discharge monitoring equipment. The charge / discharge efficiency evaluation factor in the model is adjusted based on the correlation algorithm between the efficiency factor and these parameters. For the capacity factor, the evaluation factor for the battery's available capacity is updated in the model based on the response relationship of the capacity factor, combining information such as battery health, real-time temperature, and charge / discharge rate obtained by the battery management system (BMS). For the attenuation factor, data such as the number of cycles is obtained from a counter and the intraday temperature difference is obtained from a temperature monitoring device. Based on a mathematical model of the attenuation factor and related parameters, the evaluation factor for battery life loss in the model is calibrated. Through these means, the evaluation factors of the benefit budget model are accurately updated and calibrated, making them more closely aligned with the actual operating conditions of the energy storage system.

[0036] Utilizing a calibrated and updated revenue budget model, the energy storage system's actual operating data (such as charge and discharge capacity, charge and discharge time, and current battery status) is used as input. Combined with the efficiency factor (accounting for the effects of actual temperature and charge and discharge rate on efficiency), the capacity factor (accounting for the loss of available capacity due to high-rate charge and discharge), and the attenuation factor (focusing on battery life loss due to factors such as the number of charge and discharge cycles) from the dynamic calibration factor library, the energy storage system's revenue over the entire operating cycle is comprehensively calculated according to the model's established calculation logic. By deducting charging costs and accounting for battery life loss, a true and accurate energy storage revenue value is derived, providing a reliable basis for operational decisions regarding the energy storage system.

[0037] In one possible implementation, step S210 further includes:

[0038] Step S211: analyzing the influencing parameters of the efficiency factor, capacity factor, and attenuation factor, and establishing a response influence relationship.

[0039] Step S212: Based on the influencing parameters, parameter monitoring values ​​are collected in real time.

[0040] Step S213: When the change in the parameter monitoring value exceeds a threshold, dynamically configure the factor value based on the response influence relationship, and update the corresponding factor in the dynamic calibration factor library.

[0041] Specifically, for the efficiency factor, we carefully analyze the mechanism of the effects of parameters such as temperature T, operating rate C-rate, and depth of charge and discharge DOD on it, clarifying that lower temperature will increase the internal resistance of the battery, thereby reducing the charge and discharge efficiency, and that high operating rate may cause efficiency to decrease due to polarization effects. For the capacity factor, we analyze the influence of parameters such as battery health SOH, operating rate C-rate, and temperature T. For example, a decrease in battery health will reduce the available capacity, and high temperature or high rate operation may cause capacity loss. For the attenuation factor, we study the relationship between parameters such as the number of cycles N, the intraday temperature difference ΔT, and the average depth of charge and discharge DOD_avg. For example, an increase in the number of cycles will accelerate battery aging, and a large intraday temperature difference and an excessively deep depth of charge and discharge will also aggravate attenuation. By comprehensively analyzing these parameters, we establish an accurate response and influence relationship between each factor and the corresponding influencing parameter, providing a theoretical basis for the dynamic adjustment of subsequent factor values.

[0042] Real-time monitoring is conducted around parameters influencing the efficiency factor, capacity factor, and attenuation factor. Temperature sensors are used to collect real-time temperature T values, covering the operating environment temperature of the energy storage device and the temperature of the battery itself. Current and voltage monitoring devices and related algorithms are used to obtain operating rate (C-rate) and charge / discharge depth (DOD) data, accurately tracking the current level and charge level changes during the battery charge and discharge process. The battery management system (BMS) monitors the battery health (SOH) in real time. A counter is used to record the number of cycles (N). Temperature difference measurement equipment is used to obtain the daily temperature difference (ΔT). The average charge / discharge depth (DOD_avg) is calculated based on historical charge and discharge data. Through various sensors and monitoring methods, the real-time values ​​of these influencing parameters are collected comprehensively and continuously, providing reliable data support for subsequent dynamic adjustment of factors.

[0043] Continuously monitor the collected values ​​of influencing parameters. Once it is found that the changes in efficiency factor-related parameters such as temperature T, operating rate C-rate, depth of charge and discharge DOD, or capacity factor-related parameters such as battery health SOH and temperature T, or attenuation factor-related parameters such as number of cycles N and daily temperature difference ΔT exceed the pre-set threshold, the values ​​of efficiency factor, capacity factor, and attenuation factor are immediately adjusted dynamically based on the established response influence relationship. For example, if the temperature change exceeds the threshold, the efficiency factor value is recalculated according to the established relationship. After the adjustment is completed, the new factor value is promptly updated to the dynamic calibration factor library to ensure that the factor library can always reflect the real-time operating status of the energy storage system and provide accurate data for subsequent energy storage system revenue measurement and scheduling optimization.

[0044] Step S300: Calculate energy storage benefits using the calibrated benefit budget model to obtain energy storage calculation results.

[0045] Specifically, using a dynamically calibrated profit calculation model, the collected operating data of the energy storage system, such as charge and discharge depth, operating rate, temperature, as well as calibrated key parameters such as efficiency factor, capacity factor, and attenuation factor, are combined to quantitatively calculate the profits of the energy storage system under different operating scenarios, covering dimensions such as charge and discharge efficiency loss, capacity attenuation cost, and peak-valley electricity price difference. The final output is the energy storage calculation results that reflect the economic benefits and operating losses of the energy storage system, providing data support for subsequent optimization of operating parameters and updates to scheduling strategies.

[0046] Step S400: performing operation parameter optimization feedback on the energy storage system operation data according to the energy storage measurement result, and updating the energy storage scheduling strategy.

[0047] Specifically, based on the energy storage measurement results obtained, the scheduling strategy constraints are first analyzed according to the profit target to obtain the current scheduling constraints and time series prediction constraints; then the above constraints are fed back to the energy storage system, and the optimization objective function is constructed based on the power scheduling target, and the corresponding optimization objective constraints are configured; finally, a hybrid optimization algorithm is used to search and optimize the scheduling strategy under the current scheduling constraints or time series prediction constraints. At the same time, combined with the model predictive control rolling optimization, the optimal feedback of the energy storage system operating parameters is realized, and then the energy storage scheduling strategy is updated to achieve the goal of minimizing the loss of energy storage equipment and maximizing the energy storage demand.

[0048] In one possible implementation, step S400 further includes:

[0049] Step S410: Based on the energy storage calculation results, the scheduling strategy constraints are analyzed according to the profit target to obtain the current scheduling constraints and the time series prediction constraints.

[0050] Step S420: Feedback the current scheduling constraints and the time series prediction constraints to the energy storage system, and perform scheduling strategy search and optimization under the current scheduling constraints or the time series prediction constraints.

[0051] Specifically, based on the energy storage measurement results, the scheduling strategy constraints are analyzed around the profit target. Starting from the hardware operating limitations of the current energy storage system, such as the upper limit of charge and discharge power, the battery remaining capacity threshold, and combining external conditions such as real-time electricity price signals and grid scheduling rules, the current scheduling constraints are determined. At the same time, a time series analysis model is used to predict load demand, electricity price fluctuation trends, and renewable energy output within a certain period in the future. The prediction results are converted into time series prediction constraints, such as the charge and discharge power range and capacity state constraints in the future time period. This fully obtains the dual constraints covering real-time operating limitations and future prediction boundaries, providing clear boundary conditions for subsequent scheduling strategy optimization.

[0052] A hybrid algorithm combining model predictive control (MPC) and particle swarm optimization (PSO) is used to search and optimize the scheduling strategy. First, current scheduling constraints (such as device power caps and SOC thresholds) and time series forecast constraints (such as future electricity price peak and valley periods and load forecast curves) are input into the optimization framework to construct an optimization model with a profit maximization objective function. Within each control cycle, the MPC algorithm generates a scheduling sequence for a future finite time domain based on the current system state and forecast constraints, while the PSO algorithm searches for the optimal power allocation solution by iteratively updating particle positions, where the particle positions correspond to the charge and discharge power and time windows of each energy storage device. During the search process, the algorithm verifies the constraints in real time and evaluates the strategy benefits using a fitness function. A dynamic weight adjustment mechanism is introduced to balance the priorities of current and forecast constraints. Ultimately, a scheduling strategy that satisfies the constraints and achieves the optimal benefit is output. The constraints for the next cycle are then updated through a rolling optimization mechanism, achieving dynamic optimal scheduling of the energy storage system.

[0053] In one possible implementation, step S420 further includes:

[0054] Step S421: construct an optimization objective function based on the power scheduling target.

[0055] Step S422: configuring optimization target constraints according to the current scheduling constraints and timing prediction constraints.

[0056] Step S423: Based on the optimization objective function and optimization objective constraints, a hybrid optimization algorithm is used to search for the current scheduling strategy and model predictive control rolling optimization.

[0057] Specifically, first clarify the power dispatch target, such as taking the maximization of the net profit of the energy storage system within the dispatch cycle as the core goal. From the revenue level, combined with the real-time and predicted peak and valley electricity price data of the power market, determine the price difference of charging and discharging in different time periods, multiply it by the charge and discharge volume and efficiency factor (from the dynamic calibration factor library) of the corresponding time period, and construct the revenue term; from the cost level, based on the attenuation factor in the dynamic calibration factor library, combined with the operating data such as the battery charge and discharge depth and the number of cycles, calculate the battery life loss cost and construct the attenuation cost term. Through mathematical modeling, the objective function is expressed as the total revenue within the dispatch cycle minus the total attenuation cost, that is, Among them, P ch (t) and P dis (t) is the charge and discharge power at time t, E ch (t) and E dis (t) is the charge and discharge capacity at time t, η eff (t) is the efficiency factor, C deg (t) is the attenuation cost coefficient, Deg factor (t) is the attenuation factor, N cycle (t) is the number of cycles, which converts the power scheduling target into an optimization objective function that can be quantified and calculated, providing a clear mathematical goal for subsequent strategy optimization.

[0058] First, the current scheduling constraints and time series forecasting constraints are sorted out and quantified. From the perspective of current scheduling constraints, the hardware limitations of energy storage devices, such as the upper limit of charge and discharge power, the safe range of battery state of charge (SOC), the operating temperature range of the equipment, as well as real-time grid scheduling requirements and electricity price policies, are integrated into mathematical expressions. For example, the charge and discharge power must satisfy 0≤P ch (t)≤P ch，max , 0≤P dis (t)≤P dis，max , SOC must meet SOC min ≤SOC(t)≤SOC max For time-series forecasting constraints, a time series analysis model is used to obtain load demand forecasts, electricity price fluctuation curves, and renewable energy output forecasts for future time periods. These are then converted into charging and discharging power ranges and SOC state transition constraints for each time period. For example, high electricity prices may require limiting discharge power during a certain period in the future, or a minimum charge level may be required due to peak loads. Finally, these constraints are integrated and configured as equality or inequality constraints for the optimization objective function, ensuring that the scheduling strategy consistently meets equipment operational safety, grid regulations, and future forecast boundary conditions during the search and optimization process.

[0059] The optimization objective function and optimization objective constraints are input into a hybrid optimization algorithm consisting of model predictive control (MPC) and particle swarm optimization (PSO). The particle swarm optimization algorithm encodes scheduling policy parameters such as the charge and discharge power and time window in each time period into particle positions. By iteratively updating the particle speed and position, it searches for the optimal solution globally. The objective function value is used as the fitness function to evaluate the pros and cons of the scheduling strategy corresponding to each particle. At the same time, a dynamic adjustment mechanism of inertia weight is introduced to balance global search and local development capabilities. In each control cycle, model predictive control constructs a rolling optimization problem based on the current energy storage system state (such as SOC, temperature, number of cycles, etc.) and future finite time domain forecast constraints (such as load demand, electricity price fluctuations, renewable energy output forecasts, etc.). It generates a scheduling sequence for multiple future time periods and only executes the scheduling strategy for the first time period. The optimization problem is then updated based on the latest collected operating data to achieve rolling optimization. During the search process, the hybrid optimization algorithm verifies the optimization target constraints in real time to ensure that the charging and discharging power does not exceed the equipment upper limit, the SOC is maintained in a safe range, and the grid scheduling requirements are met. It ultimately outputs the current optimal scheduling strategy that meets the constraints and maximizes the objective function. Through the rolling optimization characteristics of MPC, it continuously adapts to the real-time changes and predicted trends of the energy storage system.

[0060] Example 2, based on the same inventive concept as the intelligent power dispatching method for energy storage in the above-mentioned embodiment, Figure 2 As shown, the present application provides an intelligent power dispatching system for energy storage. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0061] The operation data acquisition module 10 is used to collect the operation data of the energy storage system, including charge and discharge depth, operation rate, temperature, cycle number, battery status, and SOC-OCV curve.

[0062] The key parameter calibration module 20 is used to dynamically calibrate the key parameters of the benefit calculation model based on the energy storage system operation data.

[0063] The energy storage calculation result acquisition module 30 is used to perform energy storage benefit calculation using the calibrated benefit budget model to obtain energy storage calculation results.

[0064] The energy storage scheduling strategy updating module 40 is used to perform operating parameter optimization feedback on the energy storage system operating data according to the energy storage measurement results, and update the energy storage scheduling strategy.

[0065] Furthermore, the system is also used to implement the following functions:

[0066] Obtain status data and energy storage demand data of energy storage devices; decompose the energy storage demand data according to the status data of the energy storage devices, search for the charge and discharge power and time window of each energy storage device with the goal of minimizing energy storage device losses and maximizing energy storage demand, and control the operating status of each energy storage device in the energy storage system.

[0067] Furthermore, the system is also used to implement the following functions:

[0068] A dynamic calibration factor library is established, including efficiency factor, capacity factor, and attenuation factor; the evaluation factor of the benefit budget model is updated and calibrated using the dynamic calibration factor library; and the energy storage benefit is calculated based on the energy storage system operation data using the updated and calibrated benefit budget model.

[0069] Furthermore, the system is also used to implement the following functions:

[0070] Analyze the influencing parameters of the efficiency factor, capacity factor, and attenuation factor to establish a response influence relationship; based on the influencing parameters, collect parameter monitoring values ​​in real time; when the change in the parameter monitoring value exceeds a threshold, dynamically configure the factor value based on the response influence relationship, and update the corresponding factor in the dynamic calibration factor library.

[0071] Furthermore, the system is also used to implement the following functions:

[0072] Based on the energy storage measurement results, the scheduling strategy constraints are analyzed according to the profit target to obtain the current scheduling constraints and the time series prediction constraints; the current scheduling constraints and the time series prediction constraints are fed back to the energy storage system, and the scheduling strategy search optimization is performed under the current scheduling constraints or the time series prediction constraints.

[0073] Furthermore, the system is also used to implement the following functions:

[0074] Based on the power dispatch target, an optimization objective function is constructed; according to the current dispatch constraints and timing prediction constraints, the optimization objective constraints are configured; based on the optimization objective function and the optimization objective constraints, a hybrid optimization algorithm is used to search for the current dispatch strategy and model predictive control rolling optimization.

[0075] Example 3, Figure 3 A schematic structural diagram of an electronic device provided for the intelligent power dispatching method for energy storage of the present invention shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 3As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 3 Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the electronic device can be connected through a bus or other means. Figure 3 The bus connection is taken as an example.

[0076] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0078] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. An intelligent power dispatching method for energy storage, characterized in that: include: Collect energy storage system operating data, including charge and discharge depth, operating rate, temperature, cycle number, battery status, and SOC-OCV curve; Dynamically calibrating key parameters of the revenue calculation model based on the energy storage system operating data; Calculate energy storage benefits through the calibrated benefit budget model to obtain energy storage calculation results; Based on the energy storage calculation results, the energy storage system operation data is optimized for operating parameters and feedback is provided to update the energy storage scheduling strategy.

2. The intelligent power dispatching method for energy storage according to claim 1, characterized in that: Collecting energy storage system operating data, previously including: Obtain status data of energy storage equipment and energy storage demand; Based on the status data of the energy storage device, the energy storage power demand is decomposed. With the goal of minimizing the loss of energy storage devices and maximizing the energy storage power demand, the charging and discharging power and time window of each energy storage device are searched to control the operating status of each energy storage device in the energy storage system.

3. The intelligent power dispatching method for energy storage according to claim 1, characterized in that: Dynamically calibrate key parameters of the revenue estimation model, including: Establish a dynamic calibration factor library, including efficiency factor, capacity factor, and attenuation factor; Using the dynamic calibration factor library to update and calibrate the evaluation factors of the revenue budget model; The updated and calibrated revenue budget model is used to calculate the energy storage revenue based on the energy storage system operating data.

4. The intelligent power dispatching method for energy storage according to claim 3, characterized in that: Establish a dynamic calibration factor library, including: Analyze the influencing parameters of the efficiency factor, capacity factor, and attenuation factor, and establish a response-influence relationship; Based on the influencing parameters, real-time acquisition of parameter monitoring values; When the change in the parameter monitoring value exceeds a threshold, the factor value is dynamically configured based on the response influence relationship, and the corresponding factor in the dynamic calibration factor library is updated.

5. The intelligent power dispatching method for energy storage according to claim 1, characterized in that: According to the energy storage measurement results, the energy storage system operation data is optimized for operating parameters and feedback is provided to update the energy storage scheduling strategy, including: Based on the energy storage calculation results, the scheduling strategy constraints are analyzed according to the profit target to obtain the current scheduling constraints and the timing prediction constraints; The current scheduling constraints and the time series prediction constraints are fed back to the energy storage system, and a scheduling strategy search and optimization is performed under the current scheduling constraints or the time series prediction constraints.

6. The intelligent power dispatching method for energy storage according to claim 5, characterized in that: Also includes: Based on the power dispatch target, construct the optimization objective function; Configuring optimization target constraints based on the current scheduling constraints and timing prediction constraints; Based on the optimization objective function and optimization objective constraints, a hybrid optimization algorithm is used to search for the current scheduling strategy and model predictive control rolling optimization.

7. An intelligent power dispatching system for energy storage, characterized in that: The system is used to implement the intelligent power dispatching method for energy storage according to any one of claims 1 to 6, and the system includes: Operation data acquisition module, used to collect energy storage system operation data, including charge and discharge depth, operation rate, temperature, cycle number, battery status, SOC-OCV curve; A key parameter calibration module, configured to dynamically calibrate key parameters of a benefit calculation model based on the energy storage system operating data; The energy storage calculation result acquisition module is used to calculate the energy storage benefits through the calibrated benefit budget model and obtain the energy storage calculation results; The energy storage scheduling strategy updating module is used to perform operating parameter optimization feedback on the energy storage system operating data according to the energy storage measurement results, and update the energy storage scheduling strategy.

8. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; Wherein, the processor is used to execute the intelligent power scheduling method for energy storage as described in any one of claims 1 to 6.

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