Dynamic power distribution method and device for direct-current micro-grid hybrid energy storage system

Abstract

The invention discloses a dynamic power distribution method and device for a direct-current micro-grid hybrid energy storage system, and relates to the technical field of electric energy storage management, and the method comprises the steps: collecting the power parameters, load demand fluctuation data and renewable energy power generation output data of each energy storage unit in the hybrid energy storage system in real time, calculating a multi-dimensional power entropy index, and calculating the power entropy index of the hybrid energy storage system; based on the indexes, power distribution weight coefficients are dynamically generated, power dynamic distribution is carried out, a charging and discharging strategy is generated, charging and discharging control of the hybrid energy storage system is executed, and power distribution is dynamically adjusted in combination with a real-time feedback mechanism. The technical problems that an existing energy storage system power distribution method lacks flexibility and cannot effectively cope with load fluctuation and unstable renewable energy sources, so that the system operation efficiency and stability are poor are solved, the charging and discharging strategy of the energy storage unit is dynamically adjusted through the multi-dimensional power entropy index and the real-time feedback mechanism, and the energy storage efficiency is improved. The technical effect of efficient and stable operation of the energy storage system is achieved.

Description

Technical Field

[0001] This invention relates to the field of power energy storage management technology, and more specifically to a power dynamic allocation method and equipment for a hybrid energy storage system in a DC microgrid. Background Technology

[0002] With the rapid development of renewable energy sources (such as solar and wind power), DC microgrid systems, as an important architecture supporting the integration of renewable energy, have been widely used in distributed generation and energy storage. However, the volatility of renewable energy and the dynamic changes in load demand in microgrids pose significant challenges to system stability and efficiency. Traditional power allocation methods for energy storage systems typically rely on fixed dispatch strategies, lacking flexibility and failing to effectively cope with system load fluctuations and the uncertainty of renewable energy generation. This can easily lead to over- or under-utilization of energy storage units, reducing the overall efficiency and stability of the system. Summary of the Invention

[0003] This application provides a power dynamic allocation method and equipment for a hybrid energy storage system in a DC microgrid, which solves the technical problem that the existing power allocation methods of energy storage systems lack flexibility, cannot effectively cope with load fluctuations and the instability of renewable energy, and result in poor system operating efficiency and stability.

[0004] The first aspect of this application provides a method for dynamic power allocation in a hybrid energy storage system for a DC microgrid. The method includes: real-time acquisition of power parameters, load demand fluctuation data, and renewable energy generation output data of each energy storage unit in the hybrid energy storage system; calculation of a multi-dimensional power entropy index based on the power parameters, load demand fluctuation data, and renewable energy generation output data, wherein the multi-dimensional power entropy index includes at least power fluctuation entropy, load demand matching entropy, and renewable energy penetration entropy; dynamic generation of power allocation weight coefficients based on the multi-dimensional power entropy indexes, and dynamic power allocation to each energy storage unit in the hybrid energy storage system based on the power allocation weight coefficients, generating a charging and discharging strategy for each energy storage unit; execution of charging and discharging control of each energy storage unit in the hybrid energy storage system based on the charging and discharging strategies, and energy status feedback through a real-time feedback mechanism; and iterative power reallocation when a change in energy status exceeds a threshold.

[0005] A second aspect of this application provides an electronic device comprising: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the method described in any of the first aspects.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The power dynamic allocation method and equipment for a hybrid energy storage system in a DC microgrid provided in this application relate to the field of power energy storage management technology. By collecting system data in real time, calculating multi-dimensional power entropy indicators, dynamically generating power allocation weight coefficients, optimizing the charging and discharging strategies of energy storage units, and dynamically adjusting power allocation in conjunction with a real-time feedback mechanism, the method achieves precise charging and discharging control of energy storage units. This solves the technical problem that existing energy storage system power allocation methods lack flexibility and cannot effectively cope with load fluctuations and the instability of renewable energy, resulting in poor system operating efficiency and stability. The method achieves the technical effect of dynamically adjusting the charging and discharging strategies of energy storage units through multi-dimensional power entropy indicators and a real-time feedback mechanism, thereby achieving efficient and stable operation of the energy storage system. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic flowchart of a power dynamic allocation method for a hybrid energy storage system in a DC microgrid, provided in an embodiment of this application.

[0010] Figure 2 This application provides a schematic diagram of the structure of an electronic device.

[0011] Explanation of reference numerals in the attached drawings: Electronic device 300, memory 301, processor 302, communication interface 303, bus architecture 304. Detailed Implementation

[0012] This application provides a power dynamic allocation method and equipment for a hybrid energy storage system in a DC microgrid, which solves the technical problem that the existing power allocation methods of energy storage systems lack flexibility, cannot effectively cope with load fluctuations and the instability of renewable energy, and result in poor system operating efficiency and stability.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides a method for dynamic power allocation in a hybrid energy storage system for DC microgrids, the method comprising:

[0016] P10: Real-time acquisition of power parameters, load demand fluctuation data, and renewable energy power generation output data of each energy storage unit in the hybrid energy storage system.

[0017] Specifically, real-time acquisition of power parameters, load demand fluctuation data, and renewable energy generation output data for each energy storage unit is the starting point of the entire dispatch optimization process. First, to ensure accurate understanding of the energy storage unit's status, it is necessary to monitor its power parameters, such as current, voltage, and output power—that is, its electrical performance parameters—in real time. This data reflects the charging and discharging status of each energy storage unit and provides necessary information for subsequent power allocation. For example, changes in lithium battery voltage and discharge current directly affect the energy storage unit's discharge strategy, ensuring the battery operates within a reasonable operating range while avoiding over-discharge or over-charging. To accurately acquire this data, high-precision current and voltage sensors and real-time data acquisition equipment can be used to transmit the collected data to the central control system.

[0018] Besides the power parameters of the energy storage units, changes in load demand are also a key factor in the operation of energy storage systems. Load demand fluctuations reflect real-time changes in system power demand, influenced by factors such as user electricity consumption patterns, seasonal variations, and weather conditions. By monitoring load demand data in real time, load fluctuation trends can be predicted in advance, allowing for dynamic adjustments to the charging and discharging schedules of energy storage units and preventing power supply and demand imbalances. For example, during nighttime or low-demand periods, energy storage units can be adjusted to charge, while during high-demand periods, they can be scheduled to discharge to meet peak loads.

[0019] Furthermore, renewable energy sources (such as solar and wind power) are a crucial component of microgrids, and their power generation is intermittent and unpredictable. Real-time acquisition of renewable energy power generation data reflects information such as power output, fluctuation frequency, and power change trends. This data helps the system dynamically assess the power generation capacity of renewable energy sources and determine whether to dispatch power from the energy storage system or supplement power from the external grid based on the current power generation. For example, when photovoltaic power generation is insufficient, the energy storage system will take on more load to ensure the stable operation of the microgrid.

[0020] To ensure the real-time nature and accuracy of data acquisition, corresponding sensors and data acquisition equipment are required. Sensors are installed in each energy storage unit, load terminal, and renewable energy generation equipment to monitor and acquire relevant power parameters, load demand fluctuation data, and renewable energy generation output data in real time. These sensors can acquire data with high precision and high frequency and transmit the data to the central control system via a communication network. Furthermore, data transmission can utilize modern communication technologies such as Wireless Sensor Networks (WSN) and the Internet of Things (IoT), which ensure low-latency and high-reliability data transmission, thereby enabling real-time monitoring of the energy storage system, load, and renewable energy generation output.

[0021] By acquiring comprehensive and real-time data, the system accurately obtains the power parameters, load demand fluctuations, and renewable energy generation data of the energy storage unit, providing precise data support for the dynamic power allocation of the energy storage system.

[0022] P20: Based on the power parameters, load demand fluctuation data, and renewable energy power generation output data, calculate a multi-dimensional power entropy index, which includes at least power fluctuation entropy, load demand matching entropy, and renewable energy penetration entropy.

[0023] Furthermore, step P20 in this embodiment of the application also includes:

[0024] P21: Apply a sliding time window to the power parameters and calculate the probability distribution of the power change rate within each time window; P22: Based on the probability distribution, calculate the power fluctuation entropy using the entropy formula, whereby the power fluctuation entropy characterizes the degree of uncertainty in the system's power dynamics; P23: Perform deviation analysis on the load demand fluctuation data and the actual output power of the energy storage system, and calculate the load demand matching entropy, whereby the load demand matching entropy characterizes the accuracy of the system's response to load changes; P24: Dynamically track the penetration rate of the renewable energy power generation output data and calculate the renewable energy penetration entropy, whereby the renewable energy penetration entropy characterizes the intensity of the volatility impact of renewable energy.

[0025] It should be understood that by calculating multi-dimensional power entropy indicators based on the power parameters, load demand fluctuation data, and renewable energy power generation output data of hybrid energy storage systems, the operating status and regulation capabilities of energy storage systems can be comprehensively evaluated from multiple perspectives, providing an important basis for subsequent dynamic power allocation.

[0026] First, a sliding time window is applied to the power parameters. This process divides the system's power data into multiple consecutive time windows, the length of which is set according to the system's requirements and operating characteristics. Within each time window, the probability distribution of the power change rate is calculated. This probability distribution reflects the trend and magnitude of power changes over the time period, providing the necessary data foundation for subsequent power fluctuation entropy calculations. The sliding time window allows the system to accurately capture short-term power fluctuations, thereby improving the prediction accuracy of power fluctuations.

[0027] Next, based on the probability distribution, the power fluctuation entropy is calculated using the entropy formula. The entropy formula is a mathematical tool for measuring system uncertainty; its calculation result, the power fluctuation entropy, characterizes the degree of uncertainty in the power dynamics of the energy storage system. Specifically, the higher the power fluctuation entropy, the more complex the power fluctuations of the system, and the greater the difficulty in prediction. This indicator allows for the quantification of the impact of power fluctuations on overall system stability and provides a basis for formulating power dispatch strategies. Typically, systems with higher power fluctuation entropy require more frequent dispatching to cope with larger power fluctuations.

[0028] Next, a deviation analysis is performed between load demand fluctuation data and the actual output power of the energy storage system, and the load demand matching entropy is calculated. The load demand matching entropy reflects the accuracy and effectiveness of the energy storage system in responding to load changes. By calculating the difference between load demand fluctuation data and the actual output power of the energy storage units, the performance of the energy storage system in meeting real-time load demand can be evaluated, and the load demand matching entropy can be obtained. The load demand matching entropy characterizes the accuracy of the energy storage system in responding to load changes; the lower the value, the more accurate the energy storage system's response to load demand, and vice versa. By calculating the load demand matching entropy, the performance of the energy storage system in meeting load demand can be quantified, providing a basis for optimizing scheduling strategies.

[0029] Next, the penetration rate of renewable energy power generation is dynamically tracked, and the renewable energy penetration entropy is calculated. The power generation of renewable energy sources (such as solar and wind power) is typically affected by environmental conditions, exhibiting strong intermittency and uncertainty. Therefore, dynamic penetration rate tracking involves real-time monitoring of the proportion of renewable energy in the system and its changing trends, and deriving the renewable energy penetration entropy by calculating the dynamic changes in the penetration rate. The renewable energy penetration entropy characterizes the strength of the impact of renewable energy volatility on system stability. A higher value indicates a greater impact of renewable energy volatility on the system, while a lower value indicates a more stable penetration of renewable energy. By calculating the renewable energy penetration entropy, the penetration of renewable energy in the energy storage system and its impact on system stability can be quantified, providing support for optimizing the coordinated operation of renewable energy and energy storage systems.

[0030] In summary, power fluctuation entropy reflects the complexity of system power fluctuations, load demand matching entropy assesses the system's ability to respond to load fluctuations, and renewable energy penetration entropy provides an important basis for assessing the impact of renewable energy fluctuations on the system. Combining these entropy values ​​allows for the development of more precise and efficient power allocation strategies, ensuring that energy storage systems can maintain stable and efficient operation under different operating environments.

[0031] P30: Dynamically generate power allocation weight coefficients based on the multi-dimensional power entropy index, and dynamically allocate power to each energy storage unit in the hybrid energy storage system based on the power allocation weight coefficients to generate charging and discharging strategies for each energy storage unit.

[0032] Furthermore, step P30 in this embodiment of the application also includes:

[0033] P31: Establish a power fluctuation entropy-weight mapping relationship, and assign a higher power fluctuation entropy weight coefficient to energy storage units with fast response speed; P32: Establish a load demand matching entropy-weight mapping relationship, and assign a higher load demand matching entropy weight coefficient to energy storage units with high energy density; P33: Establish a renewable energy penetration entropy-weight mapping relationship, and assign a higher renewable energy penetration entropy weight coefficient to energy storage units with long cycle life; P34: Normalize and merge the power fluctuation entropy weight coefficient, load demand matching entropy weight coefficient, and renewable energy penetration entropy weight coefficient to generate the final power allocation weight coefficient for each energy storage unit.

[0034] Optionally, based on the previously calculated multi-dimensional power entropy index, power allocation weighting coefficients are dynamically generated, and power is dynamically allocated to each energy storage unit in the hybrid energy storage system based on these weighting coefficients, thereby generating a charging and discharging strategy for each energy storage unit. By rationally allocating power, the advantages of each energy storage unit can be fully utilized, improving the overall performance of the energy storage system.

[0035] First, a power fluctuation entropy-weighting mapping relationship is established. Power fluctuation entropy reflects the uncertainty of system power changes. Systems with greater fluctuations require more efficient and faster-responding energy storage units for regulation. Therefore, based on the response speed of each energy storage unit, higher power fluctuation entropy weighting coefficients can be assigned to energy storage units with faster response speeds (e.g., supercapacitors and other devices that can charge and discharge rapidly in a short time). In this way, highly responsive energy storage units can be prioritized to cope with sudden power fluctuations, reducing the impact of power fluctuations on system stability.

[0036] Next, a load demand matching entropy-weight mapping relationship is established. Load demand matching entropy characterizes the accuracy of the energy storage system's response to load fluctuations. High-energy-density energy storage units (such as lithium batteries) can provide continuous power output more efficiently. Therefore, when fine-tuning load demand is required, these energy storage units can be given higher load demand matching entropy weighting coefficients. In this way, high-energy-density energy storage units are preferentially allocated to periods with higher load demand, ensuring that load fluctuations are accurately met.

[0037] Furthermore, a renewable energy penetration entropy-weight mapping relationship is established. The volatility of renewable energy generation significantly impacts power system stability; therefore, weights need to be rationally allocated based on the cycle life of energy storage units. For energy storage units with longer cycle lives (such as some deep-cycle batteries), a higher renewable energy penetration entropy weight coefficient is assigned, as these units can withstand more charge-discharge cycles without easily failing when renewable energy generation is insufficient. This strategy utilizes long-lifecycle energy storage units to address the instability of renewable energy, extending equipment lifespan and improving system reliability.

[0038] Finally, the power fluctuation entropy weighting coefficient, load demand matching entropy weighting coefficient, and renewable energy penetration entropy weighting coefficient are normalized and fused to generate the final power allocation weighting coefficient. The purpose of normalization is to adjust each weighting coefficient to the same dimension according to certain rules, avoiding excessive or insufficient influence of different indicators on the final weight. By fusing these weighting coefficients, the system can comprehensively evaluate the capacity of each energy storage unit and formulate precise power allocation strategies based on the characteristics and needs of the energy storage units.

[0039] Based on the final power allocation weight coefficients generated above, power is dynamically allocated to each energy storage unit in the hybrid energy storage system, and charging and discharging strategies for each energy storage unit are generated. This ensures the rationality and dynamism of power allocation, enabling each energy storage unit to fully leverage its advantages under different operating conditions and optimize the overall performance of the system.

[0040] Furthermore, in generating power allocation weighting coefficients, step P34 of this embodiment also includes:

[0041] P34-1a: Real-time acquisition of the State of Charge (SOC) and State of Health (SOH) of each energy storage unit; P34-2a: Calculation of the aging compensation factor for the energy storage unit based on the SOC and SOH data; P34-3a: Multiplication of the aging compensation factor with the final power allocation weight coefficient to generate the corrected power allocation weight coefficient.

[0042] In one possible embodiment of this application, the power allocation weighting coefficient can be further refined by aging compensation correction to ensure that the power allocation strategy is closer to the actual operating state and to improve the reliability and economy of the energy storage system.

[0043] Specifically, the first step is to acquire the state of charge (SOC) and state of health (SOH) of each energy storage unit in real time. SOC reflects the current charge level of the energy storage unit, while SOH characterizes its health and remaining lifespan. For example, a high SOC allows the energy storage unit to provide more power, while a high SOH indicates more stable performance and the ability to complete more charge-discharge cycles. Real-time monitoring of these two indicators helps ensure that energy storage units participate in power distribution under appropriate conditions, avoids overuse of degraded energy storage units, and improves the overall efficiency and reliability of the system.

[0044] Next, based on the real-time acquired SOC and SOH data, the aging compensation factor of the energy storage unit is calculated. The aging compensation factor primarily considers the gradual performance degradation of the energy storage unit during long-term use, especially when the number of charge-discharge cycles is high, which affects the unit's efficiency and lifespan. By analyzing the trends in SOC and SOH, an aging compensation factor can be calculated, representing the power allocation ratio that needs to be adjusted due to aging. For example, an energy storage unit with a low SOH may need to reduce its load to avoid accelerating its aging process due to over-discharge, while an energy storage unit with a low SOC may not be able to handle excessively high loads, thus requiring compensatory scheduling. The calculation formula for the aging compensation factor can be designed based on the SOC and SOH data of the energy storage unit. For example, the aging compensation factor is inversely proportional to SOH and directly proportional to SOC. This is because the lower the SOH, the worse the performance of the energy storage unit, requiring greater compensation; while the higher the SOC, the more usable energy the energy storage unit has, and the compensation factor can be appropriately reduced.

[0045] Finally, the aging compensation factor is multiplied by the previously calculated final power allocation weight coefficient to generate the corrected power allocation weight coefficient. This correction process ensures that the actual health condition and aging level of energy storage units are fully considered when participating in dynamic power allocation. For example, the power allocation weight coefficient of energy storage units with high aging compensation factors will be appropriately reduced to avoid over-utilizing their degraded components; conversely, the weight coefficient of energy storage units in good health will remain at a high level, thereby ensuring that the system maximizes the advantages of energy storage units in energy dispatch. This process not only considers the dynamic characteristics of energy storage units but also their actual operating status, providing strong support for achieving efficient and reliable energy storage system dispatch.

[0046] Furthermore, step P30 in this embodiment of the application also includes:

[0047] P35: Calculate the baseline power allocation value for each energy storage unit based on the power allocation weighting coefficient; P36: Detect the current schedulable capacity of each energy storage unit and perform a capacity feasibility verification on the baseline power allocation value; P37: When a capacity over-limit is detected, reallocate the excess power according to the weighting coefficient ratio to generate a charging and discharging strategy that meets safety constraints.

[0048] Specifically, the application of power allocation weighting coefficients can be further refined to ensure that the generated charging and discharging strategy is not only based on multi-dimensional power entropy indicators, but also meets the actual schedulable capacity constraints of each energy storage unit, thereby ensuring the safety and reliability of the system.

[0049] First, based on the power allocation weighting coefficients calculated previously, the baseline power allocation value for each energy storage unit is calculated. The baseline power allocation value represents the theoretical power load that each energy storage unit should bear under the current system conditions, and is typically related to the unit's capacity, health status, load demand, and renewable energy generation status. In this application, the characteristics of each energy storage unit and external demand are comprehensively considered, and power is allocated to each unit according to the power allocation weighting coefficients to obtain the baseline power allocation value for each unit. For example, if an energy storage unit has a fast response speed and good health status, its power allocation value may be higher to ensure that the system can quickly respond to sudden power fluctuations.

[0050] Next, capacity feasibility verification is performed, which involves detecting the current dispatchable capacity of each energy storage unit and verifying whether the baseline power allocation value conforms to the actual capacity limitations of each energy storage unit. The dispatchable capacity of an energy storage unit is typically affected by multiple factors, including the unit's remaining charge (SOC), state of health (SOH), and maximum charge / discharge capacity. This can be verified by monitoring the capacity status of each energy storage unit in real time and comparing the baseline power allocation value with the actual dispatchable capacity. If the baseline power allocation value of an energy storage unit exceeds its dispatchable capacity, it is considered that the energy storage unit cannot handle this power value and adjustment is required.

[0051] When a capacity overrun is detected, the excess power needs to be reallocated according to the power allocation weighting coefficient to generate a charging and discharging strategy that meets safety constraints. In other words, when the capacity of a particular energy storage unit is detected to be overrun, the excess power needs to be reallocated proportionally based on the power allocation weighting coefficient of each energy storage unit. For example, energy storage units with higher weighting coefficients will bear more excess power, while those with lower weighting coefficients will have their power allocation reduced. This method ensures that power demand is effectively met while preventing each energy storage unit from exceeding its maximum charging and discharging capacity.

[0052] After redistribution, the power allocation value of each energy storage unit is checked again to see if it meets its dispatchable capacity constraint, until the power allocation value of all energy storage units is within the safe range, and finally a charging and discharging strategy that meets the safety constraints is generated.

[0053] Furthermore, step P37 in this embodiment of the application also includes:

[0054] P37-1: Establish power allocation priority rules and set allocation priority coefficients for energy storage units related to power supply to critical loads; P37-2: Dynamically adjust the charging and discharging strategy according to the priority coefficients, and perform continuous power supply protection for critical loads based on the adjusted charging and discharging strategy.

[0055] Optionally, power allocation priority rules can be introduced to further optimize the power allocation of energy storage units, so as to ensure that the power supply of critical loads can be prioritized when facing fluctuations in system demand, and to ensure the continuity and stability of their power supply.

[0056] First, establish power allocation priority rules and set allocation priority coefficients for energy storage units related to critical load power supply. Critical loads refer to loads that play a vital role in the microgrid operation and whose power supply cannot be interrupted, such as emergency equipment in hospitals and critical servers in data centers. To ensure the continuity of power supply to these critical loads, the energy storage units directly related to them need to be assigned higher allocation priority and configured with corresponding allocation priority coefficients. The allocation priority coefficient is a parameter used to adjust the power allocation order; a higher value indicates a higher priority for the energy storage unit in power allocation. Its setting can be comprehensively considered based on the power allocation weight of the energy storage unit and the importance of power supply to the critical load. For example, energy storage units related to critical load power supply are assigned higher priority coefficients to ensure that these energy storage units can prioritize providing stable power to critical loads at critical times.

[0057] Next, the charging and discharging strategies of the energy storage units are dynamically adjusted according to the established priority coefficients. Specifically, based on the priority coefficients of the energy storage units, priority is given to energy storage units that supply power to critical loads when formulating charging and discharging strategies. Higher-priority energy storage units will be allocated more energy storage power to ensure that they can meet the power demands of critical loads. When the system faces capacity constraints or overload conditions, higher-priority energy storage units will be allocated power first, while lower-priority energy storage units may be scheduled to lower power outputs or have their charging operations postponed.

[0058] This dynamic adjustment strategy enables the energy storage system to flexibly adjust the power allocation of energy storage units according to the importance of the load, ensuring that the power supply to critical loads is not affected even under resource constraints. This prioritization and dynamic adjustment mechanism effectively guarantees a stable power supply to critical loads, improving the reliability and security of the system.

[0059] P40: Based on the charging and discharging strategy, the charging and discharging control of each energy storage unit in the hybrid energy storage system is executed, and the energy status is fed back through a real-time feedback mechanism. When the energy status change is detected to exceed the threshold, iterative power redistribution is performed.

[0060] Furthermore, in the embodiment of this application, step P40 further includes: Based on the aforementioned charging and discharging strategy, the charging and discharging control of each energy storage unit in the hybrid energy storage system is executed.

[0061] P41: Convert the charging and discharging strategy into specific power command values ​​for each energy storage unit; P42: Use model predictive control to continuously optimize the power command values ​​with the goal of minimizing the weighted sum of multi-dimensional power entropy; P43: Execute the optimized power command values ​​through power electronic interface devices to control the charging and discharging of each energy storage unit.

[0062] It should be understood that, based on the previously generated charging and discharging strategy, the charging and discharging control of each energy storage unit in the hybrid energy storage system is executed, and energy status feedback is performed through a real-time feedback mechanism. When a change in energy status is detected to exceed a threshold, iterative power reallocation is performed to ensure the continuous and stable operation of the energy storage system.

[0063] First, the charging and discharging strategy is translated into specific power command values ​​for each energy storage unit. The charging and discharging strategy provides the overall power demand of the energy storage units, while the power command value is the precise power output or input that each energy storage unit should perform at a specific time. This process concretizes the charging and discharging strategy, making it usable for the actual control of the charging and discharging operations of the energy storage units. The power command value for each energy storage unit is generated based on its battery state, load demand, renewable energy output, and the calculation results of the aforementioned multi-dimensional power entropy indicators, thereby achieving precise scheduling of the energy storage system.

[0064] Next, Model Predictive Control (MPC) is employed to perform rolling optimization of the power command value, aiming at minimizing the weighted sum of multi-dimensional power entropy. MPC is an advanced control method that optimizes power dispatch at the current moment by predicting the system's power demand and state changes over future time periods. By minimizing the weighted sum of multi-dimensional power entropy, the MPC method ensures that the power fluctuations of the energy storage system are minimized, while avoiding over-dispatch or imbalance, thereby improving system efficiency and stability. Rolling optimization means that the system needs to periodically update the power command value based on new measurement data and prediction results to ensure that the charging and discharging strategy of the energy storage unit is always in an optimal state.

[0065] Finally, the optimized power command values ​​are executed through power electronic interface devices to precisely control the charging and discharging of each energy storage unit. Power electronic interface devices (such as DC / DC converters and inverters) convert the control signals into specific current and voltage outputs, ensuring that the energy storage units charge or discharge according to the optimized power command values. These devices ensure that the energy storage units operate within safe limits while rapidly responding to the system's power regulation needs, thereby achieving efficient control of the energy storage units.

[0066] In addition to implementing charge and discharge control, a real-time feedback mechanism is required to monitor the energy status. Specifically, this involves continuously monitoring the power output, state of charge (SOC), and health status of each energy storage unit, as well as overall system operating parameters such as load demand and renewable energy generation output. When changes in the energy status exceed a set threshold—for example, a rapid decrease in the SOC of a storage unit, a sudden and significant increase in load demand, or a sharp decrease in renewable energy generation output—an iterative power reallocation process is triggered. This process reassesses the system's operating status, recalculates multi-dimensional power entropy indicators based on the latest data, and adjusts the charge and discharge strategy to ensure the system can adapt to dynamic changes in a timely manner and maintain stable operation.

[0067] Furthermore, step P40 in this embodiment of the application also includes:

[0068] P44: Set the threshold for power fluctuation entropy change rate, SOC balance threshold, and temperature change threshold; P45: Monitor the multi-dimensional power entropy indicators and the SOC and temperature parameters of each energy storage unit in real time; P46: When any monitored parameter exceeds the corresponding threshold, trigger the regeneration process of the charging and discharging strategy and re-execute the dynamic power allocation of each energy storage unit.

[0069] Optionally, the real-time feedback mechanism can be further refined by setting thresholds for multiple key parameters and monitoring changes in these parameters in real time to ensure that the system can adjust the charging and discharging strategy in a timely manner when key indicators exceed the safe or optimized range.

[0070] First, thresholds for the power fluctuation entropy change rate, SOC balance, and temperature change are set. These thresholds are used to monitor power fluctuations, the state of charge (SOC) of energy storage units, and temperature changes in energy storage units, respectively, to prevent instability of energy storage units or the entire system due to abnormal changes in these parameters. Specifically, the power fluctuation entropy change rate threshold monitors the rate of change of power fluctuation entropy. When the change in power fluctuation entropy exceeds the preset range, it means that the system power fluctuations are intensifying, and the scheduling strategy of energy storage units may need to be adjusted. The SOC balance threshold monitors the state of charge balance of energy storage units to ensure that the power of all energy storage units is balanced and that some energy storage units are not overcharged or over-discharged. The temperature change threshold monitors the temperature changes of energy storage units to avoid reduced efficiency or safety accidents caused by excessively high or low temperatures.

[0071] Next, multi-dimensional power entropy indicators, as well as the SOC and temperature parameters of each energy storage unit, are monitored in real time. Continuous data collection allows for timely understanding of the current operating status of the energy storage units and the overall system operation. If a parameter is detected to be close to or exceed a set threshold, it is necessary to determine whether adjustments are needed. In particular, SOC and temperature are directly related to the health and performance of the energy storage units; timely monitoring and adjustment help extend the lifespan of energy storage equipment and ensure stable system operation.

[0072] When any monitored parameter exceeds its corresponding threshold, the system will trigger a regeneration process for the charging and discharging strategy and re-execute the dynamic power allocation of each energy storage unit. The purpose of this process is to respond to abnormal system changes, readjust the charging and discharging strategies of the energy storage units, and ensure that the system maintains a safe and efficient operating state under all circumstances. For example, if the power fluctuation entropy change rate is too large, it indicates a sharp increase in system power fluctuations, which may require mobilizing energy storage units with faster response times; if the SOC balance deviation is large, it may be necessary to adjust the charging and discharging quantities of each energy storage unit to achieve power balance; if the temperature of an energy storage unit exceeds a safe threshold, the load on high-temperature energy storage units should be reduced or the temperature control system should be activated to prevent overheating damage.

[0073] Through real-time closed-loop feedback, it is possible not only to monitor the multi-dimensional system operation status in real time, but also to respond promptly when abnormal changes are detected. This ensures that the microgrid can operate continuously and stably when facing various complex situations such as load fluctuations, energy storage unit aging, and temperature changes, while improving the service life of energy storage equipment and the overall system reliability.

[0074] Furthermore, step P40 in this embodiment of the application also includes:

[0075] P41a: Construct a historical operation database to store the mapping relationship between the multi-dimensional power entropy index and the power allocation weight coefficient; P42a: Based on the mapping relationship, train a power allocation strategy optimization model through deep learning and update the calculation logic of dynamically generating power allocation weight coefficients online.

[0076] In one possible embodiment of this application, the intelligence and adaptability of the power allocation strategy can be enhanced by introducing a historical operating database and a deep learning model. This includes gradually optimizing the power allocation strategy through the accumulation of historical data and training with deep learning, thereby improving the accuracy and efficiency of dynamically generating power allocation weight coefficients.

[0077] First, a historical operation database is constructed to store the mapping relationship between multi-dimensional power entropy indicators and power allocation weighting coefficients. Over time, the system continuously collects and stores data on multi-dimensional power entropy indicators such as power fluctuations, load demand matching, and renewable energy penetration, while simultaneously recording the corresponding energy storage unit power allocation weighting coefficients to build the historical operation database. By constructing this database, a large amount of operational data can be accumulated, including power demand under different system conditions, energy storage unit performance, and changes in the external environment.

[0078] Next, by establishing a mapping relationship between multi-dimensional power entropy indicators and power allocation weight coefficients, the study analyzes how the power allocation weight coefficients change under different power fluctuations, load fluctuations, and renewable energy output conditions. This storage and accumulation of historical data can provide rich training material for deep learning models, thereby improving the accuracy of power allocation and the system's adaptability.

[0079] Furthermore, based on the mapping relationships in the historical operating database, the system trains a power allocation strategy optimization model using deep learning technology. The deep learning model can learn complex nonlinear relationships from historical data, thereby discovering potential correlation patterns between different power entropy indices and energy storage unit power allocation weight coefficients. Through training, the system can not only predict and optimize power allocation but also self-adjust its strategy to adapt to new operating conditions and changes in the external environment.

[0080] A well-trained deep learning model can continuously correct and optimize its power allocation strategy through real-time data input during online updates. This means that when the system encounters new operating scenarios, the deep learning model can quickly adjust its computational logic and generate new power allocation weight coefficients to cope with load fluctuations, changes in the health of energy storage units, and uncertainties in renewable energy generation. This adaptive optimization process enables the system to continuously improve its performance and better cope with changes and challenges in long-term operation. This not only allows the energy storage system to be efficiently scheduled under different conditions but also enables it to adapt to new demands and operating scenarios that may arise in the future through continuous learning and adjustment.

[0081] In summary, the embodiments of this application have at least the following technical effects:

[0082] This application effectively improves the stability and reliability of the energy storage system by dynamically adjusting the charging and discharging strategies of the energy storage units, ensuring the stable operation of the microgrid under load fluctuations and unstable renewable energy conditions; it precisely optimizes power allocation based on multi-dimensional power entropy indicators, improving overall operating efficiency; it avoids overcharging and discharging by monitoring the SOC and SOH of the energy storage units in real time, extending the service life of the energy storage equipment; it automatically adjusts power redistribution through a real-time feedback mechanism, ensuring continuous optimization of the system in dynamic environments; and it utilizes a deep learning model to update the power allocation strategy online, enhancing the system's adaptive and intelligent scheduling capabilities under different operating conditions.

[0083] This technology achieves the goal of dynamically adjusting the charging and discharging strategies of energy storage units through multi-dimensional power entropy indicators and real-time feedback mechanisms, thereby enabling the energy storage system to operate efficiently and stably.

[0084] Example 2: Exemplary electronic device.

[0085] The following is for reference. Figure 2 The electronic device described in the embodiments of this application is used to illustrate this application.

[0086] Based on the same inventive concept as the power dynamic allocation method for a hybrid energy storage system of a DC microgrid in the foregoing embodiments, this application also provides a power dynamic allocation system for a hybrid energy storage system of a DC microgrid, comprising: a processor coupled to a memory for storing a program, wherein when the program is executed by the processor, the system performs the steps of the method described in Embodiment 1.

[0087] The electronic device 300 includes a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, processor 302, and memory 301 can be interconnected via the bus architecture 304; the bus architecture 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus architecture 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0088] Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits used to control the execution of programs according to the present application.

[0089] Communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), wired access network, etc.

[0090] Memory 301 can be ROM or other types of static storage devices capable of storing static information and instructions, RAM or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory can exist independently and be connected to the processor via bus architecture 304. Memory can also be integrated with the processor.

[0091] The memory 301 stores computer execution instructions for implementing the scheme of this application, and the processor 302 controls the execution. The processor 302 executes the computer execution instructions stored in the memory 301, thereby realizing the power dynamic allocation method for a hybrid energy storage system of a DC microgrid provided in the above embodiments of this application.

[0092] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0093] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0094] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A power dynamic allocation method for a hybrid energy storage system in a DC microgrid, characterized in that, The method includes: Real-time acquisition of power parameters, load demand fluctuation data, and renewable energy power generation output data of each energy storage unit in the hybrid energy storage system; Based on the power parameters, load demand fluctuation data and renewable energy power generation output data, a multi-dimensional power entropy index is calculated. The multi-dimensional power entropy index includes at least power fluctuation entropy, load demand matching entropy and renewable energy penetration entropy. Power allocation weight coefficients are dynamically generated based on the multi-dimensional power entropy index, and power is dynamically allocated to each energy storage unit in the hybrid energy storage system based on the power allocation weight coefficients to generate charging and discharging strategies for each energy storage unit. The charging and discharging strategy is used to control the charging and discharging of each energy storage unit in the hybrid energy storage system, and energy status feedback is performed through a real-time feedback mechanism. When the energy status change is detected to exceed the threshold, iterative power redistribution is performed.

2. The power dynamic allocation method for a hybrid energy storage system in a DC microgrid as described in claim 1, characterized in that, Based on the aforementioned power parameters, load demand fluctuation data, and renewable energy generation output data, a multi-dimensional power entropy index is calculated, including: The power parameters are subjected to a sliding time window, and the probability distribution of the power change rate within each time window is calculated. Based on the probability distribution, the power fluctuation entropy is calculated using the entropy formula, whereby the power fluctuation entropy characterizes the degree of uncertainty in the system's power dynamics. Deviation analysis is performed on the load demand fluctuation data and the actual output power of the energy storage system to calculate the load demand matching entropy, which characterizes the accuracy of the system's response to load changes. The penetration rate of the renewable energy power generation output data is dynamically tracked, and the renewable energy penetration entropy is calculated. The renewable energy penetration entropy characterizes the intensity of the volatility impact of renewable energy.

3. The power dynamic allocation method for a hybrid energy storage system in a DC microgrid as described in claim 1, comprising dynamically generating power allocation weighting coefficients based on the multi-dimensional power entropy index, including: Establish a power fluctuation entropy-weight mapping relationship and assign a higher power fluctuation entropy weight coefficient to energy storage units with fast response speed; Establish a load demand matching entropy-weight mapping relationship and assign higher load demand matching entropy weight coefficients to energy storage units with high energy density. Establish a renewable energy penetration entropy-weight mapping relationship, and assign higher renewable energy penetration entropy weight coefficients to energy storage units with long cycle lives; The power fluctuation entropy weighting coefficient, load demand matching entropy weighting coefficient, and renewable energy penetration entropy weighting coefficient are normalized and fused to generate the final power allocation weighting coefficient for each energy storage unit.

4. The power dynamic allocation method for a hybrid energy storage system in a DC microgrid as described in claim 3, characterized in that, The generation of power allocation weighting coefficients also includes: Real-time acquisition of the state of charge (SOC) and state of health (SOH) of each energy storage unit; Based on the SOC and SOH data, the aging compensation factor of the energy storage unit is calculated. The aging compensation factor is multiplied by the final power allocation weight coefficient to generate the corrected power allocation weight coefficient.

5. The power dynamic allocation method for a hybrid energy storage system in a DC microgrid as described in claim 1, characterized in that, Based on the power allocation weighting coefficient, dynamic power allocation is performed on each energy storage unit in the hybrid energy storage system to generate a charging and discharging strategy for each energy storage unit, including: Calculate the baseline power allocation value for each energy storage unit based on the power allocation weighting coefficient. The current dispatchable capacity of each energy storage unit is detected, and the capacity feasibility of the baseline power allocation value is verified. When a capacity overrun is detected, the excess power is redistributed according to the weighting coefficient ratio to generate a charging and discharging strategy that meets safety constraints.

6. The power dynamic allocation method for a hybrid energy storage system in a DC microgrid as described in claim 5, characterized in that, Generate charging and discharging strategies for each energy storage unit, including: Establish power allocation priority rules and set allocation priority coefficients for energy storage units related to power supply to critical loads; The charging and discharging strategy is dynamically adjusted according to the priority coefficient, and the power supply of critical loads is continuously protected based on the adjusted charging and discharging strategy.

7. The power dynamic allocation method for a hybrid energy storage system in a DC microgrid as described in claim 1, characterized in that, Based on the aforementioned charging and discharging strategy, charge and discharge control of each energy storage unit in the hybrid energy storage system is executed, including: The charging and discharging strategy is converted into specific power command values ​​for each energy storage unit; Model predictive control is employed, with the goal of minimizing the weighted sum of multi-dimensional power entropy, to continuously optimize the power command value; The optimized power command values ​​are executed through power electronic interface equipment to control the charging and discharging of each energy storage unit.

8. The power dynamic allocation method for a hybrid energy storage system in a DC microgrid as described in claim 7, characterized in that, Energy status feedback is conducted through a real-time feedback mechanism. When a change in energy status is detected to exceed a threshold, iterative power reallocation is performed, including: Set thresholds for power fluctuation entropy change rate, SOC balance, and temperature change; Real-time monitoring of the multi-dimensional power entropy index and the SOC and temperature parameters of each energy storage unit; When any monitored parameter exceeds the corresponding threshold, the process of regenerating the charging and discharging strategy is triggered, and the dynamic power allocation of each energy storage unit is re-executed.

9. The power dynamic allocation method for a hybrid energy storage system in a DC microgrid as described in claim 1, characterized in that, The method further includes: Construct a historical operation database to store the mapping relationship between the multi-dimensional power entropy index and the power allocation weight coefficient; Based on the mapping relationship, a power allocation strategy optimization model is trained through deep learning, and the calculation logic for dynamically generated power allocation weight coefficients is updated online.

10. An electronic device, characterized in that, include: A processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the steps of the method as claimed in any one of claims 1 to 9.

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