Industrial and commercial energy storage power resource scheduling method and system

Abstract

This invention relates to the technical field of resource scheduling, and provides a method and system for scheduling power resources of industrial and commercial energy storage. The method includes: acquiring the operating parameters of the power conversion system and the energy storage battery; assessing the internal component stress and energy conversion efficiency deviation based on the operating parameters, and evaluating the risk of accelerated degradation; converting the internal component stress, energy conversion efficiency deviation, and accelerated degradation risk into virtual aging costs; adjusting the available power limit based on the internal component stress, energy conversion efficiency deviation, and accelerated degradation risk; subtracting the virtual aging costs from the economic benefits within a preset period to obtain net benefits; generating power scheduling instructions under the constraint of the available power limit with the goal of maximizing net benefits; and after executing the power scheduling instructions, providing feedback on the actual execution status corresponding to the power scheduling instructions to correct the calculation parameters. This invention improves the accuracy and economy of industrial and commercial energy storage systems.

Description

Technical Field

[0001] This invention relates to the technical field of resource scheduling, specifically to a method and system for scheduling industrial and commercial energy storage power resources. Background Technology

[0002] During long-term operation of commercial and industrial energy storage systems, core components such as power conversion systems (PCS) and energy storage batteries will gradually age due to the passage of time and high-intensity use. This aging leads to a decrease in the energy conversion efficiency of the PCS and an accelerated decline in the usable capacity and power of the battery.

[0003] However, the "ideal" operating model within the system often fails to reflect these real-world changes in a timely manner, causing scheduling instructions to exceed the actual capacity of the equipment. For example, the simplified PCS energy conversion efficiency model upon which the optimization engine relies may become biased over long-term operation due to the aging of power semiconductor devices, resulting in actual efficiency that does not match the model's predictions. This bias causes the optimization engine to continuously underestimate or overestimate the actual available energy when formulating charging and discharging strategies, affecting electricity trading settlement and eroding economic benefits.

[0004] Aggressive scheduling strategies interact with inaccurate equipment state models, accelerating battery degradation and aging of critical PCS components. This results in actual available power and energy levels far below expectations, and exposes the system to the risk of sudden failures. Traditional power resource allocation and scheduling methods based on static models or simple empirical rules cannot effectively balance the multiple contradictions between economic benefits, safe and stable system operation, and long-term equipment lifespan. The overall robustness, sustainability, and economy of the system are severely threatened.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This application discloses a method and system for scheduling power resources of industrial and commercial energy storage, which aims to solve the technical problems of performance degradation due to equipment aging, the inability of existing scheduling models to accurately reflect the real state of equipment, and the accelerated equipment degradation caused by aggressive scheduling strategies in industrial and commercial energy storage systems during long-term operation, which lead to loss of economic benefits, unstable operation and risk of failure.

[0007] The technical solution of this application is as follows:

[0008] In a first aspect, this application discloses a method for scheduling industrial and commercial energy storage power resources, including:

[0009] Real-time synchronous acquisition of operating parameters of the power conversion system and energy storage battery;

[0010] For power conversion systems, the stress of internal components and deviations in energy conversion efficiency are evaluated based on the operating parameters of the power conversion system.

[0011] For energy storage batteries, assess the risk of accelerated degradation based on the battery's operating parameters;

[0012] The internal component stress, energy conversion efficiency deviation, and accelerated degradation risk are converted into virtual aging costs.

[0013] The available power limit of the power conversion system and energy storage battery is adjusted based on the internal component stress, energy conversion efficiency deviation, and accelerated degradation risk.

[0014] The net profit is obtained by subtracting the virtual aging cost from the economic benefits within the preset date. With the goal of maximizing the net profit, the corresponding power dispatch instructions are generated under the constraint of the available power limit to realize the dispatch of industrial and commercial energy storage power resources.

[0015] After executing the power scheduling command, the actual execution status of the power scheduling command is fed back to adaptively adjust the calculation parameters of the virtual aging cost.

[0016] Through this technical solution, this application can transform equipment aging and performance degradation into quantifiable virtual aging costs and incorporate them into the scheduling objective of maximizing economic benefits. This effectively extends equipment lifespan while ensuring economic benefits, avoids premature equipment failure and potential fault risks caused by aggressive scheduling, and resolves the contradiction between economic benefits and equipment lifespan in the prior art.

[0017] Furthermore, the steps to translate internal component stress, energy conversion efficiency deviations, and accelerated degradation risks into virtual aging costs include:

[0018] Monitor and record the power loss of the power conversion system, the operating status of the auxiliary cooling system, and the temperature of the external radiator;

[0019] Calculate the heat transfer efficiency deviation index based on power loss, operating status, and external radiator temperature.

[0020] Based on the heat transfer efficiency deviation index, the thermal resistance parameters of the internal components of the power conversion system are corrected.

[0021] Based on the heat transfer efficiency deviation index and the corrected thermal resistance parameters, assess the potential defects of the power conversion system.

[0022] Assess the level of risk exposure based on the likelihood of potential defects and the power, duration, and frequency corresponding to the current power scheduling instructions;

[0023] The corresponding risk weighting factor is calculated based on the probability of potential defects and the degree of risk exposure.

[0024] Calculate the basic aging cost based on internal component stress, energy conversion efficiency deviation, and accelerated degradation risk.

[0025] Calculate the virtual aging cost based on the risk weighting factor and the base aging cost.

[0026] Through this technical solution, this application can more accurately assess the aging risk of power conversion systems. By introducing the heat transfer efficiency deviation index, the probability of potential defects, and the degree of risk exposure, and combining them with risk weighting factors, the calculation of virtual aging costs becomes more accurate and comprehensive, thereby improving the adaptability of scheduling strategies to the actual operating state of equipment.

[0027] Based on the above, this application further proposes that, with the goal of maximizing net profit by subtracting virtual aging costs from economic benefits within a preset period, and under the constraint of the available power limit, generating corresponding power dispatch instructions to achieve industrial and commercial energy storage power resource dispatch includes the following steps:

[0028] Identify current grid ancillary service contract requirements or user-side load guarantee priorities, and activate the corresponding non-economic operation targets;

[0029] Calculate the target satisfaction index for non-economic operating objectives;

[0030] Based on the priority of non-economic operational objectives and the objective satisfaction index, objective weighting factors are generated.

[0031] Calculate the multi-objective weighted penalty term based on the target weight factors and the degree of non-compliance of non-economic operating objectives;

[0032] Replace virtual aging costs with multi-objective weighted penalty terms;

[0033] The net benefit is obtained by subtracting the multi-objective weighted penalty term from the economic benefits within the preset date. With the goal of maximizing the net benefit, and under the constraint of the available power limit, the corresponding power dispatch instructions are generated to realize the dispatch of industrial and commercial energy storage power resources.

[0034] This technical solution introduces non-economic operational objectives, such as grid ancillary services and user-side load protection, on the basis of maximizing economic benefits. By incorporating these objectives into the scheduling optimization through a multi-objective weighted penalty term, the scheduling strategy not only considers economic efficiency but also takes into account the reliability of system operation and the ability to support the grid, thus achieving multi-objective balance optimization.

[0035] Furthermore, the step of feeding back the actual execution status of the power scheduling command after execution to adaptively adjust the calculation parameters of the virtual aging cost includes:

[0036] Receive feedback data on the actual execution status of power scheduling commands;

[0037] The actual implementation feedback data is subjected to data quality checks to obtain the feedback data quality check results.

[0038] When the feedback data quality inspection results indicate that there are anomalies, the corresponding data correction strategy is activated according to the type and degree of the anomaly;

[0039] Based on the data correction strategy, process the actual execution feedback data that contains anomalies to obtain the processed feedback data;

[0040] The processed feedback data was used to correct the calculation parameters for virtual aging costs.

[0041] Through this technical solution, this application introduces a feedback mechanism for actual execution and data quality checks, which can promptly detect and correct deviations in the virtual aging cost calculation parameters, ensuring that the scheduling model can adaptively learn and adjust, thereby improving the accuracy and robustness of the scheduling strategy and avoiding persistent errors caused by model lag.

[0042] In some preferred embodiments, the steps of translating internal component stress, energy conversion efficiency deviations, and accelerated degradation risk into virtual aging costs include:

[0043] Real-time acquisition of the operating mode, market electricity price, and ancillary service demand of industrial and commercial energy storage systems;

[0044] The weighting factors for aging indicators are determined based on the operating mode, market electricity price, and ancillary service demand.

[0045] By using aging index weighting factors, the internal component stress, energy conversion efficiency deviation, and accelerated degradation risk are weighted and summed to obtain the virtual aging cost.

[0046] Through this technical solution, this application can dynamically adjust the weight of aging indicators according to real-time operating modes, market electricity prices and ancillary service demands, so that the calculation of virtual aging costs can better reflect the current market environment and system operation requirements, thereby making the dispatch strategy more flexible and adaptable.

[0047] As an optional approach, the steps for determining the weighting factors of aging indicators based on operating mode, market electricity price, and ancillary service needs include:

[0048] Obtain current-period market electricity price forecast data and ancillary service demand forecast data;

[0049] Based on market electricity price forecast data and ancillary service demand forecast data, obtain the corresponding forecast uncertainty range;

[0050] Calculate the forecast risk factor based on the forecast uncertainty interval;

[0051] Based on the operating mode, market electricity price, ancillary service demand, and predicted risk factors, the weighting factors of aging indicators are adjusted and determined. Specifically, when the predicted uncertainty range is greater than the preset range, the weighting of aging indicators for internal component stress and accelerated degradation risk is increased, while the weighting of aging indicators for energy conversion efficiency deviation is decreased. When the predicted uncertainty range is less than the preset range, the weighting of aging indicators for energy conversion efficiency deviation is increased, while the weighting of aging indicators for internal component stress and accelerated degradation risk is decreased.

[0052] This technical solution incorporates consideration of market forecast uncertainty and dynamically adjusts the weight of aging indicators based on forecast risk factors. This makes the scheduling strategy more inclined to protect equipment lifespan when the market is volatile, and more focused on efficiency when the market is stable, thus achieving a balance between risk avoidance and economic benefits in an uncertain environment.

[0053] Building upon the above, this application further proposes a step for converting internal component stress, energy conversion efficiency deviations, and accelerated degradation risks into virtual aging costs, including:

[0054] Obtain device identification information for the power conversion system and energy storage battery;

[0055] Based on the equipment identification information, query the manufacturing batch, model, and historical operating data of the corresponding equipment;

[0056] Based on manufacturing batch, model, and historical operating data, obtain individual aging characteristic parameters of the power conversion system and energy storage battery;

[0057] Based on individual aging characteristic parameters, calibrate the calculation parameters corresponding to internal component stress, energy conversion efficiency deviation, and accelerated degradation risk.

[0058] Based on all calibrated calculation parameters, internal component stress, energy conversion efficiency deviation, and accelerated degradation risk are converted into virtual aging costs.

[0059] Through this technical solution, this application can perform refined management based on the individual differences of different equipment. By querying the manufacturing batch, model and historical operating data of the equipment, individual aging characteristic parameters can be obtained, and aging cost calculation parameters can be calibrated accordingly. This makes the assessment of virtual aging costs closer to the actual aging of the equipment, and improves the personalization and accuracy of scheduling strategies.

[0060] To enhance functionality, the steps involved in calibrating the calculation parameters corresponding to internal component stress, energy conversion efficiency deviation, and accelerated degradation risk, based on individual aging characteristic parameters, include:

[0061] Obtain the measurement error range of individual aging characteristic parameters;

[0062] Based on the measurement error range, simulated individual aging characteristic parameter samples are generated;

[0063] Using simulated individual aging characteristic parameter samples, the calculated parameters for internal component stress, energy conversion efficiency deviation, and accelerated degradation risk are calibrated to obtain a calibrated parameter set.

[0064] Calculate the confidence interval for virtual aging cost based on the calibrated parameter set;

[0065] Adjust the weight of virtual aging costs based on the confidence interval.

[0066] This technical solution introduces consideration of measurement error. By generating simulated samples and calculating confidence intervals, the uncertainty of virtual aging costs can be quantified, and the weights can be adjusted accordingly, thereby making the scheduling strategy more robust and reliable in the face of uncertainty.

[0067] To refine the scheme, the steps for calculating the confidence interval of the virtual aging cost based on the calibrated parameter set include:

[0068] Anomaly detection is performed on the calibrated parameter set to obtain the anomaly detection results;

[0069] Based on the outlier detection results, outliers in the calibrated parameter set are removed.

[0070] The confidence interval for virtual aging cost is calculated using the parameter set after removing outliers.

[0071] Through this technical solution, this application further improves the quality and reliability of the calibration parameter set by outlier detection and outlier removal, thereby making the calculation of the confidence interval of virtual aging cost more accurate and providing a more reliable basis for subsequent scheduling decisions.

[0072] Secondly, this application also discloses an industrial and commercial energy storage power resource scheduling system for performing industrial and commercial energy storage power resource scheduling, including:

[0073] The operating parameter acquisition module is used to acquire the operating parameters of the power conversion system and the energy storage battery in real time.

[0074] The operating parameter evaluation module is used to evaluate the stress of internal components and the deviation of energy conversion efficiency for a power conversion system based on the operating parameters of the power conversion system.

[0075] The degradation risk assessment module is used to assess the risk of accelerated degradation for energy storage batteries based on their operating parameters.

[0076] The aging cost conversion module is used to convert internal component stress, energy conversion efficiency deviation, and accelerated degradation risk into virtual aging costs.

[0077] The power limit adjustment module is used to adjust the available power limit of the power conversion system and energy storage battery based on internal component stress, energy conversion efficiency deviation and accelerated degradation risk.

[0078] The scheduling instruction generation module is used to subtract the virtual aging cost from the economic benefits within a preset date to obtain the net benefit. With the goal of maximizing the net benefit, under the constraint of the available power limit, the module generates the corresponding power scheduling instructions to realize the scheduling of industrial and commercial energy storage power resources.

[0079] The resource scheduling feedback module is used to provide feedback on the actual execution status of the power scheduling command after it is executed, so as to adaptively correct the calculation parameters of the virtual aging cost.

[0080] This application provides a system that can implement the above-mentioned method through this technical solution. Through modular design, it realizes intelligent scheduling of power resources of industrial and commercial energy storage systems, effectively solves problems such as equipment aging, model lag and aggressive scheduling, and improves the overall performance and economic benefits of the system.

[0081] Beneficial Effects: The commercial and industrial energy storage power resource scheduling method disclosed in this application acquires the operating parameters of the power conversion system and energy storage battery in real time, and specifically assesses the internal component stress of the power conversion system, energy conversion efficiency deviation, and accelerated degradation risk of the energy storage battery, converting these physical degradation indicators into quantifiable virtual aging costs. Based on this, the method dynamically adjusts the available power limit of the power conversion system and energy storage battery according to these assessment results, and generates power scheduling instructions under the constraint of the available power limit with the goal of maximizing net benefits. In addition, the method introduces a feedback mechanism for the actual execution of scheduling instructions to adaptively correct the calculation parameters of virtual aging costs.

[0082] Through the above technical solutions, this application effectively solves the problems in existing technologies such as performance degradation due to equipment aging in commercial and industrial energy storage systems, the inability of traditional scheduling models to accurately reflect the actual state of equipment in a timely manner, and the accelerated degradation caused by aggressive scheduling strategies. Specifically, by converting equipment aging and performance degradation into virtual aging costs, scheduling decisions can incorporate the long-term lifespan of equipment into economic optimization objectives, avoiding the drawback of sacrificing equipment lifespan in the pursuit of short-term economic benefits. Dynamically adjusting the upper limit of available power ensures that scheduling commands are always within the actual capacity of the equipment, effectively preventing equipment overload operation. The adaptive feedback mechanism allows the calculation parameters of virtual aging costs to be corrected according to actual operating conditions, improving the accuracy and robustness of the scheduling model and overcoming the lag problem of existing models. In summary, the method of this application can achieve multiple balances between economic benefits, safe and stable system operation, and long-term equipment lifespan, significantly improving the robustness, sustainability, and economy of commercial and industrial energy storage systems, and solving the technical problem that traditional scheduling methods in the prior art cannot effectively balance multiple contradictions. Attached Figure Description

[0083] Figure 1 This is a flowchart of a method for scheduling industrial and commercial energy storage power resources in one embodiment of the present invention;

[0084] Figure 2 This is a flowchart of a method for scheduling industrial and commercial energy storage power resources according to another embodiment of the present invention;

[0085] Figure 3 This is a system block diagram of an industrial and commercial energy storage power resource scheduling system according to another embodiment of the present invention;

[0086] Explanation of reference numerals in the attached figures:

[0087] 1. Industrial and commercial energy storage power resource scheduling system; 11. Operation parameter acquisition module; 12. Operation parameter evaluation module; 13. Attenuation risk assessment module; 14. Aging cost conversion module; 15. Power limit adjustment module; 16. Scheduling instruction generation module; 17. Resource scheduling feedback module. Detailed Implementation

[0088] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0089] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0090] In traditional commercial and industrial energy storage systems, core components such as power conversion systems and energy storage batteries gradually age over time and with high-intensity use, leading to decreased energy conversion efficiency and accelerated degradation of usable capacity and power. However, the "ideal" operating model within the system often fails to reflect these real changes in a timely manner, causing dispatch commands to exceed the actual capacity of the equipment, thereby accelerating equipment aging, creating a vicious cycle, and seriously threatening the robustness, sustainability, and economy of the system.

[0091] In response, this application proposes a method for scheduling industrial and commercial energy storage power resources, combining... Figure 1 As shown, it includes:

[0092] S1, real-time synchronous acquisition of the operating parameters of the power conversion system and the energy storage battery;

[0093] S2, for power conversion systems, evaluates the stress of internal components and the deviation of energy conversion efficiency based on the operating parameters of the power conversion system;

[0094] S3, for energy storage batteries, assesses the risk of accelerated degradation based on the operating parameters of the energy storage batteries;

[0095] S4 transforms internal component stress, energy conversion efficiency deviation, and accelerated degradation risk into virtual aging costs.

[0096] S5 adjusts the upper limit of available power for the power conversion system and energy storage battery based on internal component stress, energy conversion efficiency deviation, and accelerated degradation risk.

[0097] S6, subtract the virtual aging cost from the economic benefits within the preset date to obtain the net benefit, and generate the corresponding power dispatch command under the constraint of the available power limit with the goal of maximizing the net benefit, so as to realize the dispatch of industrial and commercial energy storage power resources.

[0098] S7, after executing the power scheduling instruction, feeds back the actual execution status of the power scheduling instruction to adaptively correct the calculation parameters of the virtual aging cost.

[0099] To better understand the industrial and commercial energy storage power resource scheduling method proposed in this application, the key terms involved will be explained first.

[0100] "Power conversion system" typically refers to power electronic equipment in industrial and commercial energy storage systems, such as inverters or converters. Its main function is to convert the direct current (DC) power from the energy storage battery into alternating current (AC), or to convert the AC power from the power grid into DC power to charge the battery. Its operating parameters may include input / output voltage, current, power, switching frequency, internal temperature, etc.

[0101] "Energy storage battery" refers to a device used to store electrical energy, such as a lithium-ion battery pack. Its operating parameters may include voltage, current, temperature, state of charge (SOC), and state of health (SOH).

[0102] "Internal component stress" refers to the mechanical, thermal, or electrical stress that key electronic components (such as IGBTs and capacitors) inside a power conversion system experience during operation. These stresses can affect the lifespan of the components.

[0103] "Energy conversion efficiency deviation" refers to the difference between the actual energy conversion efficiency of a power conversion system and the ideal or design efficiency. This deviation is usually caused by factors such as component aging and increased losses.

[0104] "Accelerated degradation risk" refers to the possibility that the capacity or power of an energy storage battery will degrade at a rate exceeding the normal expected level under specific operating conditions.

[0105] "Virtual aging cost" is an innovative concept introduced in this application. It quantifies the potential losses caused by equipment aging (such as maintenance costs, replacement costs, and revenue loss due to efficiency decline) into a cost to guide power scheduling decisions.

[0106] "Usable power limit" refers to the maximum charge and discharge power that the power conversion system and energy storage battery can safely and stably operate under their current healthy conditions.

[0107] The method for scheduling industrial and commercial energy storage power resources proposed in this application may include the following steps in its specific implementation:

[0108] First, the operating parameters of the power conversion system and the energy storage battery are acquired in real time. This can be achieved by deploying sensors and data acquisition units in commercial and industrial energy storage systems. For example, current sensors, voltage sensors, and temperature sensors can be used to monitor the input and output current, voltage, and internal key temperature of the power conversion system in real time, as well as the individual cell voltage, total voltage, charging and discharging current, and battery cluster temperature of the energy storage battery. This data is then synchronously collected and transmitted to a central controller or cloud platform for further processing.

[0109] Secondly, for the power conversion system, the stress of internal components and the deviation in energy conversion efficiency are evaluated based on the system's operating parameters. For example, the electrical and thermal stresses of the IGBT modules can be calculated by analyzing the current, voltage waveforms, and junction temperature data of the IGBT modules within the power conversion system, combined with a preset stress model. The deviation in energy conversion efficiency can be obtained by measuring the input and output power of the power conversion system in real time, calculating the actual efficiency, and comparing it with the factory calibration or historical benchmark efficiency.

[0110] Secondly, for energy storage batteries, the risk of accelerated degradation is assessed based on their operating parameters. For example, by monitoring parameters such as the number of charge-discharge cycles, depth of charge, average temperature, and voltage plateau changes, combined with battery aging models, the changing trends of battery capacity and internal resistance can be predicted. When the rate of change of these parameters exceeds a preset threshold, it can be determined that there is a risk of accelerated degradation.

[0111] Subsequently, the internal component stress, energy conversion efficiency deviation, and accelerated degradation risk are converted into virtual aging costs. For example, a base cost coefficient can be set for each aging indicator. Then, based on the assessed stress level, efficiency deviation, and degradation risk, these factors are multiplied by the corresponding coefficients and weighted summed to obtain a comprehensive virtual aging cost. This cost can be considered as the potential economic loss incurred by the equipment due to aging.

[0112] Next, based on internal component stress, energy conversion efficiency deviations, and the risk of accelerated degradation, the available power limits of the power conversion system and the energy storage battery are adjusted. For example, when the internal component stress of the power conversion system is too high, its maximum output power may be appropriately reduced to avoid overload operation. Similarly, when the risk of accelerated degradation of the energy storage battery is high, its maximum charge and discharge power will also be limited to extend battery life. These adjustments ensure that the equipment operates within safe limits.

[0113] Then, the net benefit is obtained by subtracting the virtual aging cost from the economic benefit within the preset date. With the goal of maximizing the net benefit, and under the constraint of the available power limit, corresponding power dispatch instructions are generated to realize the dispatch of industrial and commercial energy storage power resources. For example, the dispatch system will predict the market electricity price and load demand for a future period of time (such as one day or one week), and combine them with the current available power limit of the equipment and the virtual aging cost. Through optimization algorithms (such as linear programming, dynamic programming, etc.), it will calculate the charging and discharging strategy that maximizes the net benefit under the premise of satisfying the available power limit, thereby generating specific power dispatch instructions.

[0114] Finally, after executing the power scheduling command, the actual execution status corresponding to the command is fed back to adaptively adjust the calculation parameters of the virtual aging cost. For example, after the scheduling command is executed, the system collects data such as actual charge / discharge capacity, actual efficiency, and actual temperature, and compares them with the expected values ​​of the scheduling command. If a significant deviation is found between the actual aging status and the predicted virtual aging cost, the parameters in the virtual aging cost model will be adjusted based on the feedback data to more accurately reflect the true aging state of the equipment.

[0115] Optional, combined Figure 2 As shown, S4 involves the following steps in converting internal component stress, energy conversion efficiency deviations, and accelerated degradation risks into virtual aging costs:

[0116] S41 monitors and records the power loss of the power conversion system, the operating status of the auxiliary heat dissipation system, and the temperature of the external heat sink.

[0117] S42, calculate the heat transfer efficiency deviation index based on the power loss, the operating state, and the temperature of the external radiator;

[0118] S43, Based on the heat transfer efficiency deviation index, correct the thermal resistance parameters corresponding to the internal components of the power conversion system;

[0119] S44, assess the potential defects of the power conversion system based on the heat transfer efficiency deviation index and the corrected thermal resistance parameter;

[0120] S45, assess the degree of risk exposure based on the potential defect probability and the power, duration and frequency corresponding to the current power scheduling command;

[0121] S46, Calculate the corresponding risk weighting factor based on the potential defect probability and the risk exposure level;

[0122] S47, calculate the basic aging cost based on the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk;

[0123] S48. Calculate the virtual aging cost based on the risk weighting factor and the basic aging cost.

[0124] Monitoring and recording the power loss of the power conversion system, the operating status of the auxiliary cooling system, and the temperature of the external radiator aims to obtain key real-time data reflecting the thermal management performance of the power conversion system. Power loss is directly related to internal heat generation, the operating status of the auxiliary cooling system (e.g., fan speed, coolant flow rate) reflects its heat dissipation capacity, and the temperature of the external radiator indicates its heat dissipation efficiency. These parameters form the basis for evaluating the system's thermal stress and energy conversion efficiency.

[0125] Furthermore, based on the power loss, the operating state, and the external heat sink temperature, a heat transfer efficiency deviation index is calculated. This index quantifies the difference between the actual heat transfer efficiency of the power conversion system and the ideal or reference efficiency. For example, it can be calculated by comparing the actual heat dissipation with the theoretical heat dissipation, or the actual temperature rise with the theoretical temperature rise. Its purpose is to identify the deterioration trend of the system's thermal management performance.

[0126] Based on this, the thermal resistance parameters of the internal components of the power conversion system are corrected according to the heat transfer efficiency deviation index. Thermal resistance is a crucial indicator describing the heat dissipation capability of a component, and it changes with component aging and contamination. Dynamically correcting the thermal resistance parameters using the heat transfer efficiency deviation index allows for a more accurate reflection of the component's current actual heat dissipation performance.

[0127] Subsequently, based on the heat transfer efficiency deviation index and the corrected thermal resistance parameter, the potential for defects in the power conversion system is assessed. For example, an excessively high heat transfer efficiency deviation index or a significant increase in the corrected thermal resistance parameter may indicate potential defects such as blocked heat dissipation channels, fan failure, or deterioration of the thermal interface material. The aim is to identify potential hazards that could lead to equipment failure in advance.

[0128] Simultaneously, the risk exposure level is assessed based on the likelihood of potential defects and the power, duration, and frequency corresponding to the current power scheduling instructions. Risk exposure level refers to the probability that a potential defect will translate into an actual failure under specific operating conditions. For example, scheduling instructions involving high power, prolonged operation, or frequent start-stop cycles increase the risk of potential defect exposure. The aim is to incorporate the impact of operating conditions on equipment risk into consideration.

[0129] Therefore, a corresponding risk weighting factor is calculated based on the probability of potential defects and the degree of risk exposure. This risk weighting factor is a multiplier used to adjust the base aging cost so that it better reflects the actual risks under current operating conditions. For example, when the probability of potential defects is high and the degree of risk exposure is large, the risk weighting factor will increase accordingly.

[0130] Specifically, the basic aging cost is calculated based on the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk. The basic aging cost is the initial aging cost calculated based on an aging model under the inherent characteristics of the equipment and normal operating conditions.

[0131] Finally, the virtual aging cost is calculated based on the risk weighting factor and the base aging cost. By multiplying the base aging cost by the risk weighting factor, a more comprehensive and dynamic virtual aging cost can be obtained, which not only considers the basic aging trend of the equipment, but also incorporates the thermal management performance, potential defects, and operational risks under real-time operating conditions.

[0132] In some preferred embodiments, a specific example is given below. Suppose a commercial or industrial energy storage system needs to perform high-power discharge during the summer peak season to respond to grid ancillary service demands. Before executing this dispatch command, the system monitors in real time the power loss of the power conversion system, the operating status of the auxiliary cooling system, and the temperature of the external heat sink. For example, the monitored power loss is 50kW, the fan speed of the auxiliary cooling system is 80%, and the external heat sink temperature is 60°C. Based on this data, a heat transfer efficiency deviation index of 0.15 is calculated (indicating a 15% efficiency decrease), and the thermal resistance parameters of the IGBT modules inside the power conversion system are corrected accordingly. The corrected thermal resistance parameters show that the heat dissipation capacity of the IGBT modules has decreased, and the potential for defect is assessed as moderate.

[0133] Furthermore, considering that the current scheduling instruction requires continuous discharge at 90% rated power for 2 hours, repeated twice daily, the system assesses the risk exposure level as high. Combining the moderate potential defect probability and the high risk exposure level, a risk weighting factor of 1.2 is calculated. Simultaneously, based on internal component stress, energy conversion efficiency deviation, and accelerated degradation risk, the basic aging cost is calculated to be 100 yuan. Finally, multiplying the basic aging cost of 100 yuan by the risk weighting factor of 1.2 yields a virtual aging cost of 120 yuan. This 120 yuan virtual aging cost will be used for subsequent net benefit calculations and power scheduling instruction generation. In this way, the system can more accurately assess the impact of high-power, high-frequency operation on equipment lifespan, thus enabling a more reasonable balance between short-term economic benefits and long-term equipment health when generating scheduling instructions, avoiding accelerated equipment aging due to blindly pursuing short-term gains.

[0134] Optionally, the net benefit is obtained by subtracting the virtual aging cost from the economic benefit within a preset date. With the goal of maximizing the net benefit, and under the constraint of the available power limit, corresponding power dispatch instructions are generated to achieve the dispatch of industrial and commercial energy storage power resources. The steps include:

[0135] Identify current grid ancillary service contract requirements or user-side load guarantee priorities, and activate the corresponding non-economic operation targets;

[0136] Calculate the target satisfaction index of the non-economic operating objectives;

[0137] Based on the priority of the non-economic operation objectives and the objective satisfaction index, an objective weighting factor is generated.

[0138] Calculate a multi-objective weighted penalty term based on the target weight factors and the degree of non-satisfaction of non-economic operating objectives;

[0139] Replace the virtual aging cost with the multi-objective weighted penalty term;

[0140] The net profit is obtained by subtracting the multi-objective weighted penalty term from the economic profit within the preset date. With the goal of maximizing the net profit, a corresponding power scheduling instruction is generated under the constraint of the available power limit to realize the scheduling of industrial and commercial energy storage power resources.

[0141] Specifically, identifying current grid ancillary service contract requirements or user-side load guarantee priorities and activating corresponding non-economic operational objectives means that the system identifies non-economic objectives that need to be met based on preset rules or real-time received instructions. For example, grid ancillary service contracts may require energy storage systems to provide frequency regulation or reserve capacity during specific periods, while user-side load guarantee priorities may require providing uninterrupted power to critical equipment during grid outages. Once these objectives are identified, they are activated and become constraints or optimization items in dispatch decisions.

[0142] The target satisfaction index for calculating non-economic operational objectives can be understood as quantifying the degree to which each non-economic objective is met. For example, for frequency regulation services, the satisfaction index can be calculated based on the deviation between the actual regulated power and the contracted power requirement; for load assurance, it can be calculated based on the ratio of actual power supply time to required power supply time. This index is typically a value between 0 and 1, where 1 indicates complete satisfaction and 0 indicates complete non-satisfaction.

[0143] In practical applications, generating target weight factors based on the priority of non-economic operational objectives and the target satisfaction index means assigning different importance weights to different non-economic objectives. Priorities can be preset; for example, critical loads ensuring life safety have the highest priority, followed by production loads, and then ancillary service contracts. The target satisfaction index reflects the degree to which current objectives are met. When the satisfaction level of a certain objective is low, its corresponding weight factor may be dynamically adjusted to encourage the scheduling scheme to prioritize meeting that objective.

[0144] Furthermore, a multi-objective weighted penalty term is calculated based on the target weighting factors and the degree of non-compliance of non-economic operating targets. Here, the degree of non-compliance is the complement of the target satisfaction index (e.g., 1 minus the satisfaction index). The calculation of the multi-objective weighted penalty term aims to transform unmet non-economic targets into a quantifiable "cost," which is proportional to the priority and degree of non-compliance of the target. For example, failing to meet high-priority targets will result in a higher penalty.

[0145] Therefore, replacing the virtual aging cost with the multi-objective weighted penalty term means that the calculation of net benefit no longer only considers the virtual cost caused by equipment aging, but also quantifies the potential losses or risks caused by failure to meet non-economic objectives as a penalty term. This substitution transforms the optimization objective from simply maximizing economic benefits to a multi-objective optimization that comprehensively considers economic benefits, equipment health, and the satisfaction of non-economic objectives.

[0146] Finally, the net benefit is obtained by subtracting the multi-objective weighted penalty term from the economic benefit within the preset date range. With the goal of maximizing the net benefit, and under the constraint of the available power limit, a corresponding power scheduling instruction is generated to realize the scheduling of industrial and commercial energy storage power resources. This means that the scheduling algorithm will seek an optimal power scheduling scheme that not only brings the highest economic benefit, but also minimizes the penalties caused by failure to meet non-economic objectives, thereby achieving a balance among multiple objectives.

[0147] In some preferred embodiments, a specific example is given below. Suppose an industrial or commercial energy storage system needs to simultaneously meet the following objectives: arbitrage during peak-valley electricity price differences (economic objective), providing frequency regulation services to the grid (non-economic grid ancillary service contract requirement), and providing at least 2 hours of backup power to critical production lines in a factory in an emergency (non-economic user-side load protection priority).

[0148] Traditional scheduling methods may only focus on peak-valley arbitrage and virtual aging costs, leading to over-discharge in certain periods to maximize economic benefits, thus failing to meet the response requirements of frequency regulation or providing sufficient backup power in emergencies.

[0149] According to the scheme of this application, firstly, the system identifies and activates two non-economical operational objectives: frequency regulation service and critical production line backup power. Secondly, it calculates the target satisfaction index for these two objectives. For example, if the frequency regulation service requires 1MW of regulation power, but the system can only provide 0.8MW at a certain time, its satisfaction index is 0.8. If the critical production line backup power requires 2 hours, but the current energy storage state can only provide 1.5 hours, its satisfaction index is 0.75.

[0150] Furthermore, based on preset priorities (e.g., critical production line backup power has a higher priority than frequency regulation services) and the current satisfaction index, corresponding target weighting factors are generated. For example, the weighting factor for critical production line backup power may be higher. Then, based on these weighting factors and the target non-satisfaction level (e.g., frequency regulation non-satisfaction level is 0.2, backup power non-satisfaction level is 0.25), a multi-target weighted penalty term is calculated. This penalty term quantifies the potential losses (e.g., contract penalties, production interruption losses) resulting from failing to meet these non-economic targets.

[0151] Ultimately, when generating power dispatch instructions, the system subtracts this multi-objective weighted penalty from the economic benefits within a preset date, aiming to maximize this new "net benefit." Thus, the dispatch instructions no longer solely pursue the highest economic benefit, but rather weigh economic benefits, equipment aging costs, and the fulfillment of non-economic objectives. For example, even if peak-valley arbitrage yields higher economic benefits during a certain period, if doing so would prevent meeting the backup power requirements of high-priority critical production lines, the system might choose a dispatch scheme with slightly lower economic benefits but ensuring backup power, because the penalty for not meeting backup power requirements significantly reduces the overall "net benefit." In this way, the solution proposed in this application can generate a more balanced power dispatch instruction that aligns with actual operational needs.

[0152] Optionally, the step of feeding back the actual execution status of the power scheduling command after execution to adaptively adjust the calculation parameters of the virtual aging cost includes:

[0153] Receive feedback data on the actual execution status of power scheduling commands;

[0154] The actual implementation feedback data is subjected to data quality checks to obtain the feedback data quality check results.

[0155] When the feedback data quality inspection results indicate that there are anomalies, the corresponding data correction strategy is activated according to the type and degree of the anomaly;

[0156] Based on the data correction strategy, process the actual execution feedback data that contains anomalies to obtain the processed feedback data;

[0157] The processed feedback data was used to correct the calculation parameters for virtual aging costs.

[0158] Specifically, receiving feedback data on the actual execution of power dispatch commands refers to the system acquiring actual operational data after the power dispatch commands are executed, either in real-time or near real-time. This includes parameters such as the actual charging and discharging power, duration, voltage, current, and temperature of the energy storage battery, as well as the actual operating status and efficiency of the power conversion system. This data forms the basis for evaluating the effectiveness of dispatch commands and revising the parameters used to calculate virtual aging costs.

[0159] Data quality checks on feedback data regarding actual implementation results involve verifying the validity, completeness, consistency, and accuracy of the received feedback data. For example, this includes checking for missing values, outliers, duplicate values, or values ​​outside the reasonable range. The purpose is to ensure the reliability of subsequent data processing and parameter correction.

[0160] When the feedback data quality inspection results indicate the presence of anomalies, activating the corresponding data correction strategy based on the type and severity of the anomaly means that once an anomaly is detected during data quality inspection, the system will automatically or semi-automatically select and activate a preset data correction method based on the specific nature of the anomaly (e.g., missing data, sensor reading drift, instantaneous spikes, etc.) and its severity. For example, for missing data, an interpolation strategy can be activated; for abnormal spikes, a filtering or smoothing strategy can be activated.

[0161] According to the data correction strategy, the feedback data of actual execution that contains anomalies is processed. The processed feedback data refers to the feedback data for which the activated data correction strategy has been applied. For example, if an interpolation strategy is activated, linear interpolation, polynomial interpolation, or prediction methods based on historical data are used to fill in missing values; if a filtering strategy is activated, Kalman filtering, moving average, or other methods are used to smooth noisy data or remove abnormal spikes. The goal is to transform the raw, potentially flawed feedback data into high-quality, reliable data.

[0162] Using processed feedback data to correct the calculation parameters of virtual aging costs refers to using feedback data, after data quality checks and corrections, to update or adjust relevant calculation parameters in the virtual aging cost model. These parameters may include coefficients, thresholds, or weights related to internal component stress, energy conversion efficiency deviations, and accelerated degradation risk. The aim is to make the calculation of virtual aging costs more closely resemble actual operating conditions, thereby improving its accuracy and predictive power.

[0163] Optionally, the steps to translate internal component stress, energy conversion efficiency deviations, and accelerated degradation risks into virtual aging costs include:

[0164] Real-time acquisition of the operating mode, market electricity price, and ancillary service demand of industrial and commercial energy storage systems;

[0165] The aging index weighting factor is determined based on the operating mode, the market electricity price, and the ancillary service demand.

[0166] Using the aging index weighting factor, the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk are weighted and summed to obtain the virtual aging cost.

[0167] Specifically, real-time acquisition of the operating mode of commercial and industrial energy storage systems refers to the current operating status or strategy of the system, such as peak shaving and valley filling mode, demand response mode, ancillary service mode, or self-consumption mode. Market electricity price refers to the current or predicted electricity market transaction price, and its fluctuations directly affect the economics of energy storage system operation. Ancillary service demand refers to the real-time demand of the power grid for ancillary services such as frequency regulation, peak shaving, and backup provided by the energy storage system. Real-time acquisition of these parameters provides the basic data for subsequent dynamic adjustment and calculation of aging costs.

[0168] The determination of aging index weighting factors based on the operating mode, market electricity price, and ancillary service demand can be understood as dynamically adjusting the relative importance of three aging indicators—internal component stress, energy conversion efficiency deviation, and accelerated degradation risk—in the virtual aging cost calculation according to the current external operating environment and economic objectives of the industrial and commercial energy storage system. For example, when market electricity prices are high or ancillary service demand is urgent, the system may prefer to operate at high power to obtain economic benefits. In this case, the weight of accelerated degradation risk may be appropriately increased to more strictly limit excessive aging. Conversely, when electricity prices are low or the system is in maintenance mode, more attention may be paid to energy conversion efficiency deviation to optimize long-term operating efficiency.

[0169] In practical applications, the virtual aging cost is obtained by using the aforementioned aging index weighting factors to weight and sum the stress of internal components, the energy conversion efficiency deviation, and the accelerated decay risk. The purpose is to quantify the aging impacts from different dimensions into a unified economic cost. Through weighted summation, the combined impact of various aging factors on system lifespan and performance can be comprehensively considered and transformed into a unified index that can be used for economically optimized scheduling. This weighting method allows the virtual aging cost to more accurately reflect the true degree of aging of the system under specific operating conditions and its potential impact on future benefits.

[0170] In some preferred embodiments, a specific example is given below. Suppose a commercial or industrial energy storage system faces two typical operating scenarios within a day:

[0171] Scenario 1: During peak electricity price periods, the system primarily performs peak shaving and valley filling tasks, while the power grid has a high demand for frequency regulation ancillary services. In this scenario, the system acquires real-time data on high market electricity prices, peak shaving and valley filling operation modes, and high demand for frequency regulation ancillary services. Based on this information, the weighting factors for aging indicators are determined as follows: a higher weight for accelerated degradation risk (e.g., 0.5), a moderate weight for internal component stress (e.g., 0.3), and a lower weight for energy conversion efficiency deviation (e.g., 0.2). This means that when calculating virtual aging costs, the system will more strictly penalize operations that accelerate battery aging to protect battery life, even if this may mean sacrificing some of the benefits from short-term high power output.

[0172] Scenario 2: During off-peak electricity periods, the system primarily performs charging tasks. Market electricity prices are low, and the demand for ancillary services is not significant. In this scenario, the system obtains real-time data on low market electricity prices, charging operation modes, and low ancillary service demand. Based on this information, the weighting factors for aging indicators are determined as follows: a high weight for energy conversion efficiency deviation (e.g., 0.5), a moderate weight for internal component stress (e.g., 0.3), and a low weight for accelerated degradation risk (e.g., 0.2). This means that when calculating virtual aging costs, the system will focus more on energy conversion efficiency during charging to minimize energy loss, while being relatively lenient in its consideration of battery aging, allowing for a gentler charging approach.

[0173] By dynamically adjusting the weighting factors of aging indicators as described above, the calculation of virtual aging costs can be more flexibly adapted to different operating objectives and external environments, thereby generating more adaptive and optimized power scheduling instructions and effectively balancing economic benefits and system lifespan.

[0174] Optionally, the steps for determining the weighting factors of aging indicators based on operating mode, market electricity price, and ancillary service demand include:

[0175] Obtain current-period market electricity price forecast data and ancillary service demand forecast data;

[0176] Based on the market electricity price forecast data and the ancillary service demand forecast data, obtain the corresponding forecast uncertainty range;

[0177] Calculate the prediction risk factor based on the predicted uncertainty interval;

[0178] Based on the operating mode, the market electricity price, the ancillary service demand, and the predicted risk factor, the aging index weighting factor is adjusted and determined; wherein, when the predicted uncertainty interval is greater than a preset interval, the weighting of the aging index for the internal component stress and the accelerated degradation risk is increased, while the weighting of the aging index for the energy conversion efficiency deviation is decreased; when the predicted uncertainty interval is less than the preset interval, the weighting of the aging index for the energy conversion efficiency deviation is increased, while the weighting of the aging index for the internal component stress and the accelerated degradation risk is decreased.

[0179] Specifically, obtaining current-period market electricity price forecast data and ancillary service demand forecast data refers to acquiring forecasts of electricity price trends and specific grid demand for ancillary services over a future period (e.g., the next 24 hours or longer) through market forecasting models, historical data analysis, or third-party data services. This forecast data forms the basis for power dispatch decisions. Obtaining the corresponding forecast uncertainty interval based on the market electricity price forecast data and the ancillary service demand forecast data can be understood as a quantitative assessment of the reliability of the forecast results. For example, statistical methods (such as confidence intervals, standard deviations, etc.) can be used to represent the fluctuation range or potential error of the forecast value. The larger the forecast uncertainty interval, the higher the volatility or unreliability of the market forecast. In practical applications, calculating the forecast risk factor based on the forecast uncertainty interval means transforming the aforementioned uncertainty interval into a quantitative risk indicator. For example, a function can be set to map the width of the uncertainty interval or its deviation from the average value to the forecast risk factor; the higher the factor value, the greater the forecast risk.

[0180] Furthermore, the aging index weighting factors are adjusted and determined based on the operating mode, the market electricity price, the ancillary service demand, and the predicted risk factors. This means that when determining the weights of internal component stress, energy conversion efficiency deviation, and accelerated degradation risk in the virtual aging cost, not only the current operating mode, market electricity price, and ancillary service demand are considered, but also the risk of future market predictions is taken into account. Specifically, when the predicted uncertainty range is greater than a preset range, it indicates that the market environment has significant uncertainty or volatility. In this case, to ensure the long-term stable operation of the energy storage system and asset safety, this application tends to increase the weights of the aging indexes for internal component stress and accelerated degradation risk. This means that when calculating the virtual aging cost, more emphasis will be placed on avoiding excessive stress on the internal components of the power conversion system and preventing accelerated degradation of the energy storage battery, even if this may sacrifice some energy conversion efficiency in the short term. At the same time, the weight of the aging index for energy conversion efficiency deviation is reduced to reflect that under high uncertainty, the pursuit of efficiency should give way to the protection of equipment life. Conversely, when the predicted uncertainty range is less than a preset range, it indicates that the market prediction is relatively stable and reliable. At this point, a more proactive approach can be taken to pursue economic benefits and system efficiency. Therefore, increasing the weight of aging indicators related to energy conversion efficiency deviation encourages the system to maximize energy conversion efficiency while ensuring equipment health, thereby maximizing economic gains. Simultaneously, reducing the weight of aging indicators related to stress on internal components and the risk of accelerated degradation does not mean ignoring their importance, but rather that, under controllable risks, the conservative restrictions on these indicators can be appropriately relaxed to optimize overall operational performance.

[0181] Optionally, the steps to translate internal component stress, energy conversion efficiency deviations, and accelerated degradation risks into virtual aging costs include:

[0182] Obtain device identification information for the power conversion system and energy storage battery;

[0183] Based on the equipment identification information, query the manufacturing batch, model, and historical operating data of the corresponding equipment;

[0184] Based on the manufacturing batch, model, and historical operating data, obtain the individual aging characteristic parameters of the power conversion system and energy storage battery;

[0185] Based on the individual aging characteristic parameters, calibrate the calculation parameters corresponding to the internal component stress, the calculation parameters corresponding to the energy conversion efficiency deviation, and the calculation parameters corresponding to the accelerated decay risk.

[0186] Based on all calibrated calculation parameters, the internal component stress, the energy conversion efficiency deviation, and the accelerated decay risk are converted into virtual aging costs.

[0187] Specifically, obtaining the device identification information of power conversion systems and energy storage batteries refers to acquiring the unique identification information of each power conversion system and energy storage battery by reading its unique serial number, asset tag, or electronic identification code. This identification information is the basis for subsequent queries of detailed device data.

[0188] Specifically, querying the manufacturing batch, model, and historical operating data of the corresponding equipment based on the equipment identification information can be understood as retrieving production information (e.g., manufacturing date, production line), design information (e.g., specific model, design parameters), and all operating records since its commissioning (e.g., charge / discharge cycle count, cumulative operating time, temperature profile, fault records, etc.) related to the equipment from the equipment management system or database using the equipment identification information. This data is a key basis for assessing the individual aging characteristics of the equipment.

[0189] In practical applications, based on the manufacturing batch, model, and historical operating data, individual aging characteristic parameters of the power conversion system and energy storage battery are obtained. Specifically, this involves using detailed data and specific aging models or machine learning algorithms to analyze and extract parameters reflecting the unique aging patterns of the device. For example, for energy storage batteries, their actual capacity decay curve and internal resistance growth rate can be obtained; for power conversion systems, the lifespan loss factor and thermal resistance change trend of specific components (such as IGBTs and capacitors) can be obtained. These parameters can more accurately describe the current health status and future aging trends of individual devices.

[0190] Furthermore, calibrating the calculation parameters corresponding to the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk based on the individual aging characteristic parameters means using the obtained individual aging characteristic parameters as input to adjust the correlation coefficients, weights, or thresholds in the mathematical model or algorithm used to calculate the internal component stress, energy conversion efficiency deviation, and accelerated degradation risk. For example, if the actual capacity degradation rate of a battery is faster than predicted by a general model, the weight factor in its accelerated degradation risk calculation can be increased to better reflect the battery's true condition.

[0191] Therefore, based on all calibrated calculation parameters, the internal component stress, the energy conversion efficiency deviation, and the accelerated decay risk are converted into virtual aging costs. This means that, based on the individualized calibrated calculation parameters, the quantitative values ​​of internal component stress, energy conversion efficiency deviation, and accelerated decay risk are recalculated or updated, and finally, they are comprehensively converted into more targeted and accurate virtual aging costs.

[0192] Optionally, the steps of calibrating the calculation parameters corresponding to the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk based on the aforementioned individual aging characteristic parameters include:

[0193] Obtain the measurement error range of the individual aging characteristic parameters;

[0194] Based on the measurement error range, a sample of simulated individual aging characteristic parameters is generated;

[0195] Using simulated individual aging characteristic parameter samples, the calculated parameters of the internal component stress, the energy conversion efficiency deviation, and the accelerated decay risk are calibrated to obtain a calibrated parameter set.

[0196] Based on the calibrated parameter set, calculate the confidence interval of the virtual aging cost;

[0197] The weight of the virtual aging cost is adjusted based on the confidence interval.

[0198] Specifically, the measurement error range for obtaining individual aging characteristic parameters refers to determining the possible deviation range of these parameters through statistical analysis, sensor accuracy assessment, or historical data comparison, based on individual aging characteristic parameters of power conversion systems and energy storage batteries obtained from equipment identification information, manufacturing batches, models, and historical operating data. For example, the error range can be determined based on the fluctuations in sensor datasheets, calibration reports, or historical measurement data.

[0199] Specifically, generating simulated individual aging characteristic parameter samples based on the measurement error range can be understood as generating a series of virtual data points that may represent real individual aging characteristic parameters within a known measurement error range, using Monte Carlo simulation, random sampling, or other statistical methods. These samples aim to cover various possible values ​​of the parameters within the error range to simulate uncertainties that may be encountered in actual operation.

[0200] In practical applications, simulated individual aging characteristic parameter samples are used to calibrate the calculated parameters for internal component stress, energy conversion efficiency deviation, and accelerated degradation risk, resulting in a calibrated parameter set. Specifically, this involves using these simulated samples as input and repeatedly performing the calibration process to obtain multiple calibration results. These results collectively constitute a parameter set, reflecting the possible distribution of calculated parameters considering measurement errors.

[0201] Furthermore, based on the calibrated parameter set, the confidence interval for the virtual aging cost is calculated. This means determining the range within which the virtual aging cost might fall, using statistical methods (e.g., the Bootstrap method or confidence interval calculation for parameter estimation) based on the aforementioned parameter set. This confidence interval quantifies the degree of uncertainty in the virtual aging cost estimate.

[0202] Therefore, adjusting the weight of virtual aging costs based on the confidence interval specifically means dynamically adjusting the importance of virtual aging costs in the net profit maximization objective function based on the width or position of the calculated confidence interval for virtual aging costs. For example, when the confidence interval is wide, indicating higher uncertainty in virtual aging costs, its weight can be appropriately reduced, or a more conservative scheduling strategy can be adopted; conversely, when the confidence interval is narrow, indicating more reliable estimation of virtual aging costs, its weight can be increased, allowing it to play a greater role in scheduling decisions.

[0203] Optionally, the step of calculating the confidence interval of the virtual aging cost based on the calibrated parameter set includes:

[0204] Anomaly detection is performed on the calibrated parameter set to obtain the anomaly detection results;

[0205] Based on the outlier detection results, outliers in the calibrated parameter set are removed.

[0206] The confidence interval for the virtual aging cost is calculated using the parameter set after removing outliers.

[0207] Specifically, outlier detection refers to using statistical methods or machine learning algorithms to identify observations in the calibrated parameter set that significantly deviate from other data points. For example, distance-based methods (such as K-nearest neighbors), density-based methods (such as Local Outlier Factor (LOF), statistical methods (such as the 3σ principle and box plots), or isolation-based methods (such as isolated forests) can be used. The aim is to identify outlier data points that may negatively impact the accuracy of confidence interval calculations.

[0208] The outlier detection result can be understood as the output of the outlier detection algorithm, indicating which data points in the calibrated parameter set were identified as outliers. This result can be a Boolean array, an outlier index list, or an outlier score list, used for subsequent outlier removal operations.

[0209] In practical applications, the removal of outliers from the calibrated parameter set specifically involves removing identified abnormal data points from the calibrated parameter set based on outlier detection results. For example, if the outlier detection result indicates that a parameter value is far outside the normal range, it is removed from the parameter set used to calculate the confidence interval. The purpose is to cleanse the data and ensure that subsequent confidence interval calculations are based on more reliable and representative data.

[0210] Furthermore, calculating the confidence interval for the virtual aging cost using the parameter set after removing outliers refers to calculating the confidence interval using the remaining, purified parameter set after removing outlier data points. For example, bootstrap, Monte Carlo simulation, or parametric statistical methods (such as the t-distribution) can be used to calculate the confidence interval. The aim is to obtain a more accurate and robust confidence interval for the virtual aging cost, thereby providing a more reliable basis for subsequently adjusting the weights of the virtual aging cost based on the confidence interval.

[0211] This application also discloses a commercial and industrial energy storage power resource scheduling system for performing commercial and industrial energy storage power resource scheduling, combined with... Figure 3 As shown, the industrial and commercial energy storage power resource dispatch system 1 includes:

[0212] The operating parameter acquisition module 11 is used to acquire the operating parameters of the power conversion system and the operating parameters of the energy storage battery in real time.

[0213] The operating parameter evaluation module 12 is used to evaluate the stress of internal components and the deviation of energy conversion efficiency for the power conversion system based on the operating parameters of the power conversion system.

[0214] The degradation risk assessment module 13 is used to assess the risk of accelerated degradation for energy storage batteries based on their operating parameters.

[0215] Aging cost conversion module 14 is used to convert the internal component stress, the energy conversion efficiency deviation and the accelerated decay risk into virtual aging cost;

[0216] The power limit adjustment module 15 is used to adjust the available power limit of the power conversion system and the energy storage battery according to the internal component stress, the energy conversion efficiency deviation and the accelerated degradation risk.

[0217] The scheduling instruction generation module 16 is used to subtract the virtual aging cost from the economic benefits within a preset date to obtain the net benefit. With the goal of maximizing the net benefit, under the constraint of the available power limit, it generates a corresponding power scheduling instruction to realize the scheduling of industrial and commercial energy storage power resources.

[0218] The resource scheduling feedback module 17 is used to feed back the actual execution status of the power scheduling instruction after execution, so as to adaptively correct the calculation parameters of the virtual aging cost.

[0219] To better understand the industrial and commercial energy storage power resource scheduling system proposed in this application, the following provides a detailed description of each module involved.

[0220] First, the operating parameter acquisition module is used to synchronously acquire the operating parameters of the power conversion system and the energy storage battery in real time. The above embodiments have already described the real-time synchronous acquisition of the operating parameters of the power conversion system and the energy storage battery, and will not be repeated here. It is important to emphasize that at the system level, this function is implemented through the operating parameter acquisition module. This module can be configured to include a series of sensors and data acquisition units, such as current sensors, voltage sensors, and temperature sensors, to monitor in real time the input and output current, voltage, and internal key point temperature of the power conversion system, as well as the individual cell voltage, total voltage, charging and discharging current, and battery cluster temperature of the energy storage battery. These sensors can be connected to the data acquisition unit via wired or wireless means. The data acquisition unit is responsible for converting the acquired analog signals into digital signals and transmitting them to the central controller or cloud platform via a communication interface (such as Modbus, CAN, Ethernet, etc.). As an optional implementation, the operating parameter acquisition module can also communicate with the control units (such as the PCS controller and BMS) built into the power conversion system and the energy storage battery to directly acquire their internally processed operating parameters.

[0221] Secondly, the operating parameter evaluation module is used to evaluate the internal component stress and energy conversion efficiency deviation of the power conversion system based on the operating parameters of the power conversion system. The evaluation of internal component stress and energy conversion efficiency deviation for the power conversion system has already been described in the above embodiments and will not be repeated here. It is important to emphasize that at the system level, this function is implemented through the operating parameter evaluation module. This module can be configured to include one or more processors, such as microcontrollers, digital signal processors (DSPs), or field-programmable gate arrays (FPGAs), to execute preset evaluation algorithms. For example, this module can receive current, voltage waveforms, and junction temperature data of the IGBT modules inside the power conversion system, and combine this data with a stress model stored within the module to calculate the electrical and thermal stresses of the IGBTs. The evaluation of energy conversion efficiency deviation can be obtained by measuring the input and output power of the power conversion system in real time, calculating the actual efficiency, and comparing it with the factory calibration or historical benchmark efficiency stored within the module. As a preferred implementation, the operating parameter evaluation module can be integrated into the local controller of the power conversion system to achieve distributed evaluation, or it can operate as a software module of a central scheduling system.

[0222] Secondly, the degradation risk assessment module is used to assess the risk of accelerated degradation for energy storage batteries based on their operating parameters. The assessment of accelerated degradation risk for energy storage batteries has already been described in the above embodiments and will not be repeated here. It is important to emphasize that at the system level, this function is implemented through the degradation risk assessment module. This module can be configured to include one or more processors for executing battery aging models and risk assessment algorithms. For example, this module can receive parameters such as the number of charge-discharge cycles, depth of charge, average temperature, and voltage plateau changes of the energy storage battery, and combine them with the battery aging model stored within the module to predict the changing trends of battery capacity and internal resistance. When the rate of change of these parameters exceeds a preset threshold, it can be determined that there is a risk of accelerated degradation. As an optional implementation, the degradation risk assessment module can run as part of the battery management system (BMS) or as an independent software service of the central scheduling system, obtaining the necessary data through communication with the BMS.

[0223] Subsequently, the aging cost conversion module is used to convert internal component stress, energy conversion efficiency deviation, and accelerated degradation risk into virtual aging costs. The conversion of internal component stress, energy conversion efficiency deviation, and accelerated degradation risk into virtual aging costs has already been described in the above embodiments and will not be repeated here. It is important to emphasize that at the system level, this function is implemented through the aging cost conversion module. This module can be configured to include a processor and a storage unit, storing preset cost coefficients, weighting functions, or machine learning models. For example, this module can receive the internal component stress, energy conversion efficiency deviation, and accelerated degradation risk output by the operation parameter evaluation module, and set a basic cost coefficient for each aging indicator according to the preset cost function. Then, based on the assessed stress level, efficiency deviation degree, and degradation risk magnitude, it multiplies by the corresponding coefficient and performs a weighted summation to obtain a comprehensive virtual aging cost. As a preferred implementation, this module can employ a rule-based expert system or a fuzzy logic system to dynamically adjust the cost coefficients according to different aging degrees and operating modes.

[0224] Next, the power limit adjustment module is used to adjust the available power limit of the power conversion system and the energy storage battery based on internal component stress, energy conversion efficiency deviation, and accelerated degradation risk. The above embodiments have already described the adjustment of the available power limit of the power conversion system and the energy storage battery based on internal component stress, energy conversion efficiency deviation, and accelerated degradation risk, and will not be repeated here. It is important to emphasize that at the system level, this function is implemented through the power limit adjustment module. This module can be configured to include a processor and control logic to dynamically calculate and issue new power limit commands based on the aging indicators output by the aging cost conversion module. For example, when the internal component stress of the power conversion system is too high, the module will calculate an appropriately reduced maximum output power value and send it to the power conversion system controller via the communication interface to avoid overload operation. Similarly, when the accelerated degradation risk of the energy storage battery is high, the module will also calculate and issue commands to limit its maximum charge and discharge power to extend battery life. As an optional implementation, the power limit adjustment module can be tightly integrated with the local controller of the power conversion system and the energy storage battery to achieve rapid response and fine-grained control.

[0225] Then, the scheduling instruction generation module is used to subtract the virtual aging cost from the economic benefits within a preset date to obtain the net benefit. With the goal of maximizing the net benefit, and under the constraint of the available power limit, it generates corresponding power scheduling instructions to realize the scheduling of industrial and commercial energy storage power resources. The above implementation has already described the process of subtracting the virtual aging cost from the economic benefits within a preset date to obtain the net benefit, and generating corresponding power scheduling instructions with the goal of maximizing the net benefit under the constraint of the available power limit; this will not be repeated here. It is important to emphasize that at the system level, this function is implemented through the scheduling instruction generation module. This module can be configured to include a high-performance processor and an optimization algorithm library, such as linear programming, dynamic programming, mixed-integer programming, or reinforcement learning algorithms. This module receives data from external sources such as market electricity price forecasts and load demand forecasts, as well as the available power limit from the power limit adjustment module and the virtual aging cost from the aging cost conversion module. Based on these inputs, the module uses optimization algorithms to calculate the charging and discharging strategy that maximizes the net benefit while satisfying the available power limit, thereby generating specific power scheduling instructions. As a preferred implementation, the scheduling instruction generation module can be deployed on a cloud server, utilizing powerful computing resources for complex optimization calculations.

[0226] Finally, the resource scheduling feedback module is used to provide feedback on the actual execution status of the power scheduling command after execution, so as to adaptively correct the calculation parameters of the virtual aging cost. The above implementation has already described the process of providing feedback on the actual execution status of the power scheduling command after execution to adaptively correct the calculation parameters of the virtual aging cost, and will not be repeated here. It is important to emphasize that at the system level, this function is implemented through the resource scheduling feedback module. This module can be configured to include data acquisition, data processing, and parameter correction logic. For example, after the scheduling command is executed, this module collects data such as actual charge / discharge capacity, actual efficiency, and actual temperature, and compares them with the expected values ​​of the scheduling command. If a significant deviation is found between the actual aging status and the predicted virtual aging cost, the parameters in the virtual aging cost model will be adjusted based on the feedback data to more accurately reflect the true aging state of the equipment. As an optional implementation, the resource scheduling feedback module can employ machine learning algorithms, such as regression models or neural networks, to adaptively learn and update model parameters using historical data and feedback data.

[0227] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for scheduling commercial energy storage power resources, characterized in that, include: Real-time synchronous acquisition of operating parameters of the power conversion system and energy storage battery; For power conversion systems, the stress of internal components and deviations in energy conversion efficiency are evaluated based on the operating parameters of the power conversion system. For energy storage batteries, assess the risk of accelerated degradation based on the battery's operating parameters; The internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk are converted into virtual aging costs. The available power limit of the power conversion system and the energy storage battery is adjusted based on the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk. The net profit is obtained by subtracting the virtual aging cost from the economic benefits within the preset date. With the goal of maximizing the net profit, a corresponding power scheduling instruction is generated under the constraint of the available power limit to realize the scheduling of industrial and commercial energy storage power resources. After executing the power scheduling instruction, the actual execution status corresponding to the power scheduling instruction is fed back to adaptively correct the calculation parameters of the virtual aging cost.

2. The method for scheduling industrial and commercial energy storage power resources according to claim 1, characterized in that, The step of converting the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk into virtual aging costs includes: Monitor and record the power loss of the power conversion system, the operating status of the auxiliary cooling system, and the temperature of the external radiator; Calculate the heat transfer efficiency deviation index based on the power loss, the operating state, and the external radiator temperature. Based on the heat transfer efficiency deviation index, the thermal resistance parameters of the internal components of the power conversion system are corrected. Based on the heat transfer efficiency deviation index and the corrected thermal resistance parameters, assess the potential defects of the power conversion system. Assess the degree of risk exposure based on the likelihood of potential defects and the power, duration, and frequency of the current power scheduling command. Based on the likelihood of the potential defect and the degree of risk exposure, the corresponding risk weighting factor is calculated; The basic aging cost is calculated based on the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk. The virtual aging cost is calculated based on the risk weighting factor and the base aging cost.

3. The method for scheduling industrial and commercial energy storage power resources according to claim 1, characterized in that, The step of feeding back the actual execution status of the power scheduling instruction after execution, so as to adaptively correct the calculation parameters of the virtual aging cost, includes: Receive feedback data on the actual execution status of the power scheduling command; The actual execution feedback data is subjected to data quality checks to obtain the feedback data quality check results. When the feedback data quality inspection result indicates an anomaly, the corresponding data correction strategy is activated according to the type and degree of the anomaly. According to the data correction strategy, the feedback data of the actual execution situation that has anomalies is processed to obtain the processed feedback data; The processed feedback data is used to correct the calculation parameters of the virtual aging cost.

4. The method for scheduling industrial and commercial energy storage power resources according to claim 1, characterized in that, The step of converting the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk into virtual aging costs includes: Real-time acquisition of the operating mode, market electricity price, and ancillary service demand of industrial and commercial energy storage systems; The aging index weighting factor is determined based on the operating mode, the market electricity price, and the ancillary service demand. Using the aging index weighting factor, the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk are weighted and summed to obtain the virtual aging cost.

5. The method for scheduling industrial and commercial energy storage power resources according to claim 4, characterized in that, The step of determining the aging index weighting factor based on the operating mode, the market electricity price, and the ancillary service demand includes: Obtain current-period market electricity price forecast data and ancillary service demand forecast data; Based on the market electricity price forecast data and the ancillary service demand forecast data, obtain the corresponding forecast uncertainty range; Calculate the prediction risk factor based on the predicted uncertainty interval; Based on the operating mode, the market electricity price, the ancillary service demand, and the predicted risk factor, the aging index weighting factor is adjusted and determined; wherein, when the predicted uncertainty interval is greater than a preset interval, the weighting of the aging index for the internal component stress and the accelerated degradation risk is increased, while the weighting of the aging index for the energy conversion efficiency deviation is decreased; when the predicted uncertainty interval is less than the preset interval, the weighting of the aging index for the energy conversion efficiency deviation is increased, while the weighting of the aging index for the internal component stress and the accelerated degradation risk is decreased.

6. The method for scheduling industrial and commercial energy storage power resources according to claim 1, characterized in that, The step of converting the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk into virtual aging costs includes: Obtain device identification information for the power conversion system and energy storage battery; Based on the equipment identification information, query the manufacturing batch, model, and historical operating data of the corresponding equipment; Based on the manufacturing batch, model, and historical operating data, obtain the individual aging characteristic parameters of the power conversion system and energy storage battery; Based on the individual aging characteristic parameters, calibrate the calculation parameters corresponding to the internal component stress, the calculation parameters corresponding to the energy conversion efficiency deviation, and the calculation parameters corresponding to the accelerated decay risk. Based on all calibrated calculation parameters, the internal component stress, the energy conversion efficiency deviation, and the accelerated decay risk are converted into virtual aging costs.

7. The method for scheduling industrial and commercial energy storage power resources according to claim 6, characterized in that, The step of calibrating the calculation parameters corresponding to the internal component stress, the energy conversion efficiency deviation, and the accelerated degradation risk based on the individual aging characteristic parameters includes: Obtain the measurement error range of the individual aging characteristic parameters; Based on the measurement error range, a sample of simulated individual aging characteristic parameters is generated; Using simulated individual aging characteristic parameter samples, the calculated parameters of the internal component stress, the energy conversion efficiency deviation, and the accelerated decay risk are calibrated to obtain a calibrated parameter set. Based on the calibrated parameter set, calculate the confidence interval of the virtual aging cost; The weight of the virtual aging cost is adjusted based on the confidence interval.

8. The method for scheduling industrial and commercial energy storage power resources according to claim 7, characterized in that, The step of calculating the confidence interval of the virtual aging cost based on the calibrated parameter set includes: Anomaly detection is performed on the calibrated parameter set to obtain the anomaly detection results; Based on the outlier detection results, outliers in the calibrated parameter set are removed. The confidence interval for the virtual aging cost is calculated using the parameter set after removing outliers.

9. A commercial and industrial energy storage power resource scheduling system, used to perform commercial and industrial energy storage power resource scheduling, characterized in that, include: The operating parameter acquisition module is used to acquire the operating parameters of the power conversion system and the energy storage battery in real time. The operating parameter evaluation module is used to evaluate the stress of internal components and the deviation of energy conversion efficiency for a power conversion system based on the operating parameters of the power conversion system. The degradation risk assessment module is used to assess the risk of accelerated degradation for energy storage batteries based on their operating parameters. An aging cost conversion module is used to convert the internal component stress, the energy conversion efficiency deviation, and the accelerated decay risk into virtual aging costs. The power limit adjustment module is used to adjust the available power limit of the power conversion system and the energy storage battery based on the internal component stress, the energy conversion efficiency deviation and the accelerated degradation risk. The scheduling instruction generation module is used to subtract the virtual aging cost from the economic benefits within a preset date to obtain the net benefit. With the goal of maximizing the net benefit, under the constraint of the available power limit, the module generates the corresponding power scheduling instruction to realize the scheduling of industrial and commercial energy storage power resources. The resource scheduling feedback module is used to feed back the actual execution status of the power scheduling instruction after execution, so as to adaptively correct the calculation parameters of the virtual aging cost.

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