A SOC balancing control method for DC microgrid energy storage system

Abstract

The present invention discloses a SOC balancing control method for a DC microgrid energy storage system, which relates to the field of electrical control technology. The method comprises: receiving an energy storage operation task of a DC microgrid energy storage system, wherein the task comprises energy storage task attributes, expected capacity and expected duration; initializing SOC balancing analysis of multiple energy storage units, and establishing energy storage priority constraints in combination with task attributes; formulating an energy storage control strategy space that satisfies a first strategy capacity constraint according to priority constraints and task attributes; performing multi-feature SOC balancing optimization on the strategy space, and constructing an energy storage strategy space that satisfies a second strategy capacity constraint; further optimizing based on the SOC balancing fitness function, obtaining an energy storage control strategy optimization result, and encrypting and sending the result to the DC microgrid energy storage system to execute the energy storage operation task. Thus, the technical effects of improving balancing efficiency, reducing balancing costs, and optimizing microgrid performance are achieved.

Description

Technical Field

[0001] The present invention relates to the field of electrical control technology, and in particular to a SOC balancing control method for a DC microgrid energy storage system. Background Art

[0002] In modern power systems, microgrids have become an important means of utilizing renewable energy due to their flexibility and efficiency. In DC microgrids, energy storage systems (such as battery packs) help balance power supply and demand, improving system stability and reliability.

[0003] However, during actual use, battery cells in a battery pack often experience SOC imbalances due to manufacturing variations, temperature fluctuations, and operating conditions. Existing SOC balancing control methods primarily include passive balancing and active balancing. Passive balancing achieves balancing by converting electrical energy into heat and dissipating it, while active balancing requires complex balancing circuits and strategies, resulting in low balancing efficiency and high balancing costs, which impact energy storage performance. Summary of the Invention

[0004] The present invention provides a SOC balancing control method for a DC microgrid energy storage system to solve the technical problems in the prior art of low balancing efficiency and high balancing cost, which affect the performance of the microgrid, and achieve the technical effects of improving balancing efficiency, reducing balancing cost, and optimizing microgrid performance.

[0005] The present invention provides a SOC balancing control method for a DC microgrid energy storage system, comprising:

[0006] An energy storage operation task of a DC microgrid energy storage system is received, wherein the DC microgrid energy storage system includes multiple energy storage units, and the energy storage operation task includes energy storage task attributes, expected energy storage capacity, and expected energy storage duration.

[0007] An initial SOC balance analysis is performed on the multiple energy storage units, and energy storage operation priority constraints are established in combination with the energy storage task attributes.

[0008] Based on the energy storage operation priority constraint, an energy storage control decision is made on the DC microgrid energy storage system according to the energy storage task attributes, the expected energy storage capacity and the expected energy storage duration, and a first energy storage control strategy space that satisfies the first strategy capacity constraint is established.

[0009] Based on the multi-feature SOC balance constraint, the first energy storage control strategy space is optimized for multi-feature SOC balance, and a second energy storage control strategy space that meets the second strategy capacity constraint is established.

[0010] Based on the SOC balance fitness function, the SOC balance fitness of the second energy storage control strategy space is optimized to obtain an energy storage control strategy optimization result.

[0011] The energy storage control strategy optimization result is encrypted and sent to the DC microgrid energy storage system, and the DC microgrid energy storage system performs the energy storage operation task according to the energy storage control strategy optimization result.

[0012] The present invention discloses a SOC balancing control method for a DC microgrid energy storage system, which solves the technical problems of low balancing efficiency and high balancing cost that affect microgrid performance, and achieves the technical effects of improving balancing efficiency, reducing balancing cost, and optimizing microgrid performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flow chart of a SOC balancing control method for a DC microgrid energy storage system according to the present invention;

[0014] Figure 2 The present invention is a flow chart of establishing energy storage operation priority constraints in a SOC balancing control method for a DC microgrid energy storage system. DETAILED DESCRIPTION

[0015] The technical solutions provided in the embodiments of the present invention are designed to address the technical issues of low balancing efficiency and high balancing cost in the prior art, which affect microgrid performance. The overall approach adopted is as follows:

[0016] First, the system receives and parses energy storage tasks, including task attributes, expected storage capacity, and expected storage duration. It then performs an initial SOC (State of Charge) balance analysis on multiple energy storage units to ensure that the SOC of each unit is balanced. Next, it establishes priority constraints for energy storage tasks based on the task attributes to guide subsequent energy storage control decisions. Based on the priority constraints, the system then makes energy storage control decisions for the DC microgrid energy storage system according to the task attributes, expected capacity, and duration, forming a first energy storage control strategy space that satisfies the first strategy capacity constraint. Furthermore, under the multi-feature SOC balance constraint, it performs a multi-feature SOC balance joint optimization on the first energy storage control strategy space to form a second energy storage control strategy space that satisfies the second strategy capacity constraint. Furthermore, based on the SOC balance fitness function, it performs SOC balance fitness optimization on the second energy storage control strategy space to obtain the optimal energy storage control strategy. Finally, the optimization results of the energy storage control strategy are encrypted and sent to the DC microgrid energy storage system, which performs the energy storage operation task according to the received optimization results.

[0017] Through this series of steps, we ensure that the energy storage operation tasks of the DC microgrid energy storage system can be effectively executed and controlled to achieve the optimal performance of the energy storage system.

[0018] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0019] Figure 1 The present invention is a flow chart of a SOC balancing control method for a DC microgrid energy storage system, wherein the method comprises:

[0020] An energy storage operation task of a DC microgrid energy storage system is received, wherein the DC microgrid energy storage system includes multiple energy storage units, and the energy storage operation task includes energy storage task attributes, expected energy storage capacity, and expected energy storage duration.

[0021] Specifically, a DC microgrid is a small power grid system consisting of multiple energy storage units, power generation devices (such as solar and wind power), and loads. Each energy storage unit is responsible for storing a certain amount of electrical energy and performing charging and discharging operations based on system needs. Optionally, the multiple energy storage units can be different types of battery packs (such as lithium-ion batteries, lead-acid batteries, etc.) or other energy storage devices.

[0022] Specifically, a storage task is a specific task assigned by the DC microgrid system to an energy storage unit to guide charging or discharging operations to meet grid operational requirements. These tasks include specific execution parameters and objectives, such as task attributes, expected storage capacity, and expected storage duration.

[0023] Specifically, the energy storage task attributes define the type or purpose of the task, which can be categorized as energy storage charging or energy storage discharging. The expected energy storage capacity is the target capacity specified in the energy storage task, representing the amount of electricity the system hopes to store, and can be expressed in kilowatt-hours (kWh) or megawatt-hours (MWh). The expected energy storage duration specifies how long the system expects the energy storage unit to complete the charging or discharging task. This duration can be determined based on the current needs of the microgrid and fluctuations in electricity prices. By accurately setting the expected energy storage capacity and duration, the charging and discharging capabilities of the energy storage unit can be maximized, avoiding power waste and ensuring that the system completes the task in the shortest possible time while maintaining profitability.

[0024] Among them, the energy storage operation task is the root task when multiple energy storage units are regarded as an overall energy storage network. The energy storage operation task needs to be reasonably decomposed and allocated to multiple energy storage units to realize the energy storage operation.

[0025] An initial SOC balance analysis is performed on the multiple energy storage units, and energy storage operation priority constraints are established in combination with the energy storage task attributes.

[0026] Specifically, before starting the energy storage operation task, a balanced analysis is performed on the SOC of multiple energy storage units to help the DC microgrid system reasonably distribute charging or discharging tasks, avoid individual energy storage units being in extremely high or low SOC states for a long time, reduce battery aging and capacity decay, extend system life and improve energy storage efficiency, and at the same time, maximize the use of the available capacity of each unit.

[0027] Specifically, the energy storage task attributes directly affect the priority of energy storage operations. If the energy storage task attribute is specified as a charging task, energy storage units with lower SOC should be prioritized for charging to ensure efficient task completion while preventing overcharging of units with higher SOC. If the task attribute is discharging, energy storage units with higher SOC should be prioritized for discharging to ensure sufficient energy is available for release while preventing over-discharging of units with lower SOC.

[0028] Specifically, the energy storage operation priority constraint is a task execution priority set for the energy storage unit, which is used to ensure that the energy storage system can efficiently execute tasks according to established rules and avoid resource waste or excessive load.

[0029] Optionally, the energy storage task priority constraint is not fixed, but is updated in real time with changes in the SOC state and dynamic adjustment of task attributes to ensure the execution efficiency of the energy storage task and the safety of the battery.

[0030] In some embodiments, as Figure 2 As shown, the SOC balance analysis of the multiple energy storage units is initialized, and energy storage operation priority constraints are established in combination with the energy storage task attributes, including:

[0031] Collect real-time SOC parameters corresponding to the multiple energy storage units to obtain multiple unit SOC information; perform pairwise deviation evaluation based on the multiple unit SOC information to obtain multiple SOC deviation coefficients; perform centralized value calculation based on the multiple SOC deviation coefficients to generate an SOC deviation equilibrium coefficient; determine whether the SOC deviation equilibrium coefficient is greater than / equal to the SOC deviation equilibrium threshold; if the SOC deviation equilibrium coefficient is greater than / equal to the SOC deviation equilibrium threshold, based on the energy storage task attributes, divide the multiple energy storage units into energy storage scheduling priorities according to the multiple unit SOC information to generate the energy storage operation priority constraint.

[0032] Specifically, the SOC parameters of each energy storage unit are collected in real time through sensors or the energy storage management system interface. This collected SOC data is then aggregated into SOC information for multiple units. The SOC difference between each pair of energy storage units is then analyzed, and the degree of SOC deviation between different pairs of energy storage units is calculated. This is used to assess the state of charge differences between the energy storage units, and the quantified results of the deviation are output as multiple SOC deviation coefficients.

[0033] Optionally, any two energy storage units are selected from the collected SOC information to form an energy storage unit pair. This selection is repeated until all possible energy storage unit pairs are obtained. Then, for each pair of energy storage units, the SOC difference is calculated to obtain an SOC deviation value. Next, the degree of deviation corresponding to each of the multiple SOC deviation values ​​is calculated, i.e., the deviation ratio of the energy storage unit to the SOC value, to obtain multiple SOC deviation coefficients.

[0034] Furthermore, a centralized value calculation is performed on all SOC deviation coefficients to obtain a final SOC deviation equilibrium coefficient, which represents the overall SOC equilibrium state of the system. The centralized value can be calculated using an average, weighted average, median, or other statistical methods. The relationship between the SOC deviation equilibrium coefficient and the SOC deviation equilibrium threshold is then determined. If the SOC deviation equilibrium coefficient is greater than or equal to the SOC deviation equilibrium threshold, it indicates that the SOC distribution of multiple energy storage units within the DC microgrid system is unbalanced, requiring scheduling priority division. Accordingly, if the SOC deviation equilibrium coefficient is less than the threshold, the SOC is considered to be in an acceptable equilibrium state.

[0035] Specifically, energy storage units are prioritized based on the attributes of the current energy storage task (such as charging or discharging, expected capacity, and expected duration), combined with SOC information. For example, during charging tasks, units with lower SOCs are given higher priority to ensure task completion while achieving SOC balance. During discharging tasks, units with higher SOCs are prioritized to avoid the risks associated with excessively high SOCs. Ultimately, the priority division results are output as energy storage operation priority constraints, which can be represented as a sequence of multiple energy storage units and used to guide the scheduling and task allocation of specific energy storage units.

[0036] The described method steps, through SOC balance analysis and scheduling priority division, provide a basis for subsequently rationally arranging energy storage tasks according to the actual SOC status and task requirements of the energy storage units, thereby helping to improve system efficiency and extend the life of the energy storage units.

[0037] Based on the energy storage operation priority constraint, an energy storage control decision is made on the DC microgrid energy storage system according to the energy storage task attributes, the expected energy storage capacity and the expected energy storage duration, and a first energy storage control strategy space that satisfies the first strategy capacity constraint is established.

[0038] Specifically, according to the real-time energy storage task requirements, combined with factors such as the priority and SOC status of the energy storage unit, a suitable energy storage unit is selected for charging or discharging operations, and energy storage control decisions are made, thereby establishing multiple energy storage control strategies that meet the first strategy capacity constraints, thereby generating a corresponding first energy storage control strategy space.

[0039] The first policy capacity constraint refers to the minimum storage or discharge capacity that the energy storage system must guarantee when performing energy storage tasks. This constraint ensures that the DC microgrid system can meet the specified energy storage requirements. The energy storage control policy space is the set of policies followed by the system when executing control decisions. This policy space is based on parameters such as the energy storage unit status, task attributes, expected capacity, and duration, ensuring the system can flexibly respond to various energy storage tasks.

[0040] In some embodiments, based on the energy storage operation priority constraint, an energy storage control decision is made for the DC microgrid energy storage system according to the energy storage task attributes, the expected energy storage capacity, and the expected energy storage duration, to establish a first energy storage control strategy space that satisfies a first strategy capacity constraint, including:

[0041] Based on the energy storage task attributes, energy storage control constraint information of the multiple energy storage units is collected to obtain multiple energy storage control constraint domains; based on the energy storage operation priority constraint, energy storage control decisions are made for the multiple energy storage units according to the multiple energy storage control constraint domains to obtain an energy storage control solution space, wherein the energy storage control solution space includes multiple energy storage control solutions, and each energy storage control solution includes multiple energy storage control decisions corresponding to the multiple energy storage units; with the expected energy storage capacity and the expected energy storage duration as energy storage control cleaning constraints, feature cleaning is performed on the energy storage control solution space to obtain the first energy storage control strategy space that meets the first strategy capacity constraint.

[0042] Specifically, the system first reads the attributes of the current energy storage task to determine whether it is a charging or discharging task, the expected total capacity, and the execution duration. It then determines the energy storage control constraints for multiple energy storage units, such as SOC limits, charge / discharge rate limits, and temperature limits, and generates multiple energy storage control constraint domains. These domains define the safe operating state ranges for the multiple energy storage units.

[0043] Specifically, energy storage control decisions are made for multiple energy storage units by combining energy storage operation priority constraints with multiple energy storage control constraint domains. First, the energy storage operation priority constraints determine which energy storage units will be prioritized for the current task. Then, based on the priority order and the control constraint domain of each energy storage unit, control parameters such as the charge and discharge rate and target SOC are determined for each energy storage unit, and the output is multiple energy storage control decisions.

[0044] Optionally, the real-time operational capacity of the DC microgrid system, i.e., the maximum current charge / discharge capacity, is determined based on the SOC values ​​of multiple energy storage units. Then, based on the expected energy storage capacity of the current energy storage task, the target SOC of the DC microgrid system is calculated, i.e., the overall SOC value of the DC microgrid system after the task is completed.

[0045] Specifically, based on the control constraint domain, random adjustments are made to the constraints, such as adjusting the charge / discharge rate from 1C to 0.8C, and multiple energy storage control decisions are updated for multiple energy storage units. These multiple sets of updated energy storage control decisions correspond to multiple energy storage control schemes, forming the aforementioned energy storage control scheme space, where each scheme meets all constraints of the current energy storage task. Establishing this energy storage control decision and strategy space helps the DC microgrid flexibly dispatch energy storage units based on actual conditions, ensuring that tasks are completed within the specified timeframe while maintaining capacity and safety limits.

[0046] In some implementations, the energy storage control solution space is feature cleansed using the expected energy storage capacity and the expected energy storage duration as energy storage control cleaning constraints to obtain the first energy storage control strategy space that satisfies the first strategy capacity constraint, including:

[0047] According to the energy storage control scheme space, a first energy storage control scheme is extracted; based on the first energy storage control scheme, the energy storage capacity of the DC microgrid energy storage system is predicted to obtain a first predicted energy storage capacity; based on the first energy storage control scheme, the energy storage duration of the DC microgrid energy storage system is predicted to obtain a first predicted energy storage duration; it is determined whether the first predicted energy storage capacity is greater than / equal to the expected energy storage capacity, and whether the first predicted energy storage duration is less than / equal to the expected energy storage duration; if the first predicted energy storage capacity is greater than / equal to the expected energy storage capacity, and the first predicted energy storage duration is less than / equal to the expected energy storage duration, the first energy storage control scheme is recorded as a first energy storage control strategy, and the first energy storage control strategy is added to the first energy storage control strategy space; based on the energy storage control cleaning constraint, the feature cleaning of the energy storage control scheme space is continued until the strategy capacity of the first energy storage control strategy space meets the first strategy capacity constraint.

[0048] Specifically, first, an energy storage control scheme is randomly extracted from the energy storage control scheme space as a preliminary screening object to obtain a first energy storage control scheme; then, based on the first energy storage control scheme, the total energy storage capacity of the energy storage system after the execution of the scheme is predicted to determine whether it meets the capacity requirements of the task. The predicted total energy storage capacity is the first predicted energy storage capacity, and the prediction method includes capacity prediction based on the characteristic curves of multiple energy storage units. At the same time, based on the first energy storage control scheme, the total duration of the energy storage task after the execution of the scheme is predicted as the first predicted energy storage duration. Then, it is judged whether the first predicted energy storage capacity reaches the expected capacity of the energy storage task and whether the first predicted energy storage duration is completed within the allowed time. If both are met, the scheme is recorded as the first energy storage control strategy. On the contrary, if any one of them is not met, the scheme is not included in the energy storage strategy space, and other schemes are screened until all qualified strategies are added to the first energy storage control strategy space.

[0049] The above method steps can efficiently screen out energy storage control strategies that meet the requirements of energy storage tasks through capacity and duration prediction and cleaning constraints.

[0050] Based on the multi-feature SOC balance constraint, the first energy storage control strategy space is optimized for multi-feature SOC balance, and a second energy storage control strategy space that meets the second strategy capacity constraint is established.

[0051] In some embodiments, performing a multi-feature SOC balance joint optimization on the first energy storage control strategy space based on the multi-feature SOC balance constraint to establish a second energy storage control strategy space that satisfies the second strategy capacity constraint includes:

[0052] The multi-feature SOC balance constraint includes an SOC balance stability feature constraint, an SOC balance response feature constraint, and an SOC balance loss feature constraint; according to the first energy storage control strategy space, extract the nth energy storage control strategy, wherein n is a positive integer; according to the nth energy storage control strategy, simulate and control the DC microgrid energy storage system to obtain an nth energy storage simulation SOC state data set, wherein the nth energy storage simulation SOC state data set includes multiple energy storage simulation SOC state data corresponding to the multiple energy storage units; perform a multi-feature SOC balance evaluation based on the nth energy storage simulation SOC state data set to obtain The nth strategy SOC balance evaluation result; determining whether the nth strategy SOC balance evaluation result satisfies the multi-feature SOC balance constraint; if the nth strategy SOC balance evaluation result satisfies the multi-feature SOC balance constraint, recording the nth energy storage control strategy as the nth energy storage control optimization strategy, and adding the nth energy storage control optimization strategy to the second energy storage control strategy space; continuing to perform multi-feature SOC balance joint optimization on the first energy storage control strategy space according to the multi-feature SOC balance constraint until the optimization strategy capacity of the second energy storage control strategy space satisfies the second strategy capacity constraint.

[0053] Specifically, the multi-feature SOC balancing constraint is used to measure the degree of state of charge (SOC) balance between energy storage units, and on this basis, comprehensively evaluate various characteristics of the charging and discharging process to achieve a multi-dimensional balancing effect.

[0054] Specifically, the multi-feature SOC balancing constraints include: SOC balancing stability feature constraints, which define the range of SOC that the energy storage unit needs to maintain during the entire charging and discharging process, which is used to avoid overcharging and discharging, extend the life of the unit and maintain system balance. SOC balancing response feature constraints are used to evaluate the response speed of the energy storage unit in the energy storage task, ensuring that each unit can respond quickly according to the task requirements, especially when the load fluctuates, to provide timely power support. Optionally, it is determined based on the temperature of the energy storage unit and the corresponding charge and discharge rate (rate). SOC balancing loss feature constraints are used to evaluate the energy loss of each energy storage unit during the charging and discharging process to reduce charging and discharging losses and improve the overall efficiency of the system.

[0055] Specifically, the nth energy storage control strategy is extracted from the first energy storage control strategy space, where n is a positive integer representing a different strategy number. Then, the charging and discharging process of the energy storage unit is simulated to predict the SOC change and obtain the nth energy storage simulation SOC state data set, which contains the SOC change data corresponding to each energy storage unit during the simulation process, such as the initial SOC, the SOC after charging and discharging, etc.

[0056] Then, based on the simulated SOC data, each energy storage control strategy is evaluated to see if it achieves SOC equilibrium, performing a multi-dimensional evaluation based on characteristics such as stability, responsiveness, and loss. If the SOC equilibrium evaluation result satisfies all characteristic constraints, the strategy is identified as a qualified energy storage control strategy. The nth energy storage control strategy that meets the multi-characteristic SOC equilibrium constraints is recorded as the nth energy storage control optimization strategy and added to the second energy storage control strategy space for subsequent energy storage system scheduling.

[0057] Specifically, energy storage control strategies are continuously extracted from the first energy storage control strategy space, and simulation and multi-feature SOC balance evaluation are performed on each energy storage control strategy until a sufficient number of strategies are found (i.e., satisfying the second strategy capacity constraint), thereby forming a second energy storage control strategy space.

[0058] In some implementations, performing a multi-feature SOC balance evaluation based on the nth energy storage simulation SOC state data set to obtain an nth strategy SOC balance evaluation result includes:

[0059] Connect the DC microgrid energy storage system, load a sample energy storage control SOC state data set, a sample energy storage control SOC balance stability evaluation data set, a sample energy storage control SOC balance response evaluation data set and a sample energy storage control SOC balance loss evaluation data set; use the sample energy storage control SOC state data set as input data and the sample energy storage control SOC balance stability evaluation data set as output data to train the BP neural network and construct a SOC balance stability feature evaluation model; train the SOC balance response feature evaluation model based on the sample energy storage control SOC state data set and the sample energy storage control SOC balance response evaluation data set; train the SOC balance loss feature evaluation model based on the sample energy storage control SOC state data set and the sample energy storage control SOC balance loss evaluation data set; connect the SOC balance stability feature evaluation model, the SOC balance response feature evaluation model and the SOC balance loss feature evaluation model to generate a multi-feature SOC balance evaluation model; based on the nth energy storage simulation SOC state data set and according to the multi-feature SOC balance evaluation model, output the nth strategy SOC balance evaluation result.

[0060] Specifically, sample data sets for model training are loaded from historical data, including data sets of characteristics such as energy storage control SOC state, SOC balance stability, SOC balance responsiveness, and SOC balance loss. Among them, the sample energy storage control SOC state data set includes the historical SOC state data of each energy storage unit, which is the model input. The sample energy storage control SOC balance stability evaluation data set contains the historical SOC stability evaluation results, which is the model output data set. The sample energy storage control SOC balance response evaluation data set records the response speed and efficiency of the energy storage unit to the control instructions, which is the model output data set. The sample energy storage control SOC balance loss evaluation data set: includes the energy loss data generated during the charging and discharging process of the energy storage unit, which is the model output data set.

[0061] Specifically, the neural network model uses historical SOC state data as input, and the equilibrium stability evaluation results corresponding to the historical SOC states as output. The BP neural network is trained to construct a SOC equilibrium stability characteristic evaluation model. Exemplarily, this includes preprocessing the input data, including normalization, denoising, and filling missing values, to ensure data quality and consistency. Through forward and backpropagation steps, the network weights and biases are continuously adjusted to ensure that the model's predictions are as close to the actual results as possible. Simultaneously, a portion of data not used in training is used to verify the model's performance and evaluate its accuracy, stability, and generalization capabilities.

[0062] Specifically, based on the same method principle as that of constructing the SOC equilibrium stability characteristic evaluation model, the SOC equilibrium response characteristic evaluation model and the SOC equilibrium loss characteristic evaluation model are constructed using the sample energy storage control SOC state data set and the sample energy storage control SOC equilibrium response evaluation data set, and the sample energy storage control SOC state data set and the sample energy storage control SOC equilibrium loss evaluation data set respectively. It should be understood that for the sake of brevity of the specification, no further explanation will be given here.

[0063] Based on the SOC balance fitness function, the SOC balance fitness of the second energy storage control strategy space is optimized to obtain an energy storage control strategy optimization result.

[0064] Furthermore, the second energy storage control strategy space is further optimized to obtain the optimal energy storage control strategy to ensure the best SOC balance control effect.

[0065] In some embodiments, performing SOC balance fitness optimization on the second energy storage control strategy space based on the SOC balance fitness function to obtain an energy storage control strategy optimization result includes:

[0066] According to the second energy storage control strategy space, extract the first energy storage control optimization strategy; based on the first energy storage control optimization strategy, calculate the first SOC equilibrium fitness according to the SOC equilibrium fitness function; according to the second energy storage control strategy space, extract the second energy storage control optimization strategy; based on the second energy storage control optimization strategy, calculate the second SOC equilibrium fitness according to the SOC equilibrium fitness function; based on the first SOC equilibrium fitness and the second SOC equilibrium fitness, optimize the current SOC equilibrium fitness of the first energy storage control optimization strategy and the second energy storage control optimization strategy to determine the current winning SOC equilibrium fitness and the current winning energy storage control strategy; based on the SOC equilibrium fitness function and the current winning SOC equilibrium fitness, continue to iteratively optimize the current winning energy storage control strategy until the number of iterative optimizations meets the iterative optimization number threshold, and generate the energy storage control strategy optimization result.

[0067] Specifically, a first energy storage control optimization strategy is extracted from the second energy storage control strategy space. The first energy storage control optimization strategy is an initial optimization object and includes control parameters of multiple energy storage units, such as charge and discharge rates, durations, etc.

[0068] Specifically, the SOC equilibrium fitness function measures the performance of energy storage control strategies in terms of SOC equilibrium. This function comprehensively considers multiple characteristics, such as SOC stability, response speed, and energy loss, to determine equilibrium fitness. This equilibrium fitness is a quantitative evaluation of energy storage control optimization strategies, making it easier to intuitively compare the advantages and disadvantages of different energy storage control optimization strategies.

[0069] Specifically, a second energy storage control optimization strategy is extracted from the second energy storage control strategy space, and the second energy storage control optimization strategy is used for comparison and optimization with the first strategy. The first energy storage control optimization strategy and the second energy storage control optimization strategy are respectively input into the SOC equilibrium fitness function to obtain a first SOC equilibrium fitness and a second SOC equilibrium fitness.

[0070] Furthermore, the first SOC equilibrium fitness is compared with the second SOC equilibrium fitness, and the strategy with higher fitness is selected as the current winning energy storage control strategy, and its corresponding fitness value is recorded as the current winning SOC equilibrium fitness. Then, new strategies are continuously extracted from the second energy storage control strategy space and compared and optimized with the current winning strategy. In each iteration, the strategy with higher SOC equilibrium fitness is selected and the current winning energy storage control strategy is updated until the preset iteration number threshold is reached or no new strategy can surpass the fitness of the current winning strategy. The final current winning energy storage control strategy is the final result of the energy storage control strategy optimization, which is recorded as the energy storage control strategy optimization result.

[0071] The above method steps screen out the optimal energy storage control strategy through the SOC balance fitness function and iterative optimization process to ensure that each energy storage unit achieves the best balance during the charging and discharging process.

[0072] Furthermore, the SOC equilibrium fitness function is:

[0073] ;

[0074] Among them, SOCF represents the SOC equilibrium fitness, exp represents the exponential function with the natural constant e as the base, g(SOCS) represents the normalized energy storage control SOC equilibrium stability evaluation coefficient, g(SOCA) represents the normalized energy storage control SOC equilibrium response evaluation coefficient, g(SOCL) represents the normalized energy storage control SOC equilibrium loss evaluation coefficient, and α, β, and γ represent the SOC equilibrium fitness calculation weights.

[0075] Specifically, the SOC equilibrium fitness calculation weight determines the importance of each evaluation coefficient in the SOC equilibrium fitness calculation. For example, if α is large, the SOC equilibrium stability evaluation coefficient will have a greater impact on the SOC equilibrium fitness.

[0076] Specifically, the above evaluation coefficients are all normalized to eliminate the dimension and range differences of the data so that different evaluation coefficients can be compared on the same scale.

[0077] The above function comprehensively quantifies the balancing effects of various energy storage control strategies, thereby helping to optimize the SOC equilibrium state of the energy storage system. Furthermore, by adjusting the weight parameters α, β, and γ, the importance of each evaluation coefficient in the SOC equilibrium fitness calculation can be adjusted according to actual needs, making the strategy more in line with actual needs and thus adapting to the different requirements of DC microgrid systems.

[0078] The energy storage control strategy optimization result is encrypted and sent to the DC microgrid energy storage system, and the DC microgrid energy storage system performs the energy storage operation task according to the energy storage control strategy optimization result.

[0079] Specifically, when the optimization results of the energy storage control strategy are sent to the DC microgrid energy storage system, they must be encrypted to ensure data security and integrity and prevent third-party interception or tampering. Depending on the security requirements of the target DC microgrid system, symmetric or asymmetric encryption algorithms can be selected, including AES and RSA.

[0080] Specifically, after receiving the encrypted packet, the DC microgrid energy storage system confirms the completion of the data transmission and verifies the data integrity. It then decrypts the packet using the corresponding key based on the encryption algorithm identifier in the packet, extracting the energy storage control strategy. Based on the charge and discharge instructions for each energy storage unit, it adjusts the operating status of the energy storage unit and executes the energy storage task.

[0081] In summary, the SOC balancing control method for a DC microgrid energy storage system provided by the present invention has the following technical effects:

[0082] The system receives energy storage tasks from a DC microgrid energy storage system, which includes multiple energy storage units. The energy storage tasks include energy storage task attributes, expected energy storage capacity, and expected energy storage duration. Initial SOC balancing analysis is performed on the multiple energy storage units, and energy storage task priority constraints are established based on the energy storage task attributes. Based on the energy storage task priority constraints, energy storage control decisions are made for the DC microgrid energy storage system according to the energy storage task attributes, expected energy storage capacity, and expected energy storage duration, thereby constructing a first energy storage control strategy space that satisfies the first strategy capacity constraint. A multi-feature SOC balancing joint optimization is performed on the first energy storage control strategy space using the multi-feature SOC balancing constraint to form a second energy storage control strategy space that satisfies the second strategy capacity constraint. The SOC balancing fitness function is used to optimize the SOC balancing fitness of the second energy storage control strategy space, resulting in an energy storage control strategy optimization result. The energy storage control strategy optimization result is encrypted and transmitted to the DC microgrid energy storage system, which then executes the energy storage task based on the strategy optimization result. This improves balancing efficiency, reduces balancing costs, and optimizes microgrid performance.

[0083] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A SOC balancing control method for a DC microgrid energy storage system, characterized in that: The method comprises: Receiving an energy storage operation task of a DC microgrid energy storage system, wherein the DC microgrid energy storage system includes a plurality of energy storage units, and the energy storage operation task includes energy storage task attributes, expected energy storage capacity, and expected energy storage duration; Performing initialization SOC balance analysis on the multiple energy storage units, and establishing energy storage operation priority constraints in combination with the energy storage task attributes; Based on the energy storage operation priority constraint, an energy storage control decision is made on the DC microgrid energy storage system according to the energy storage task attributes, the expected energy storage capacity and the expected energy storage duration, and a first energy storage control strategy space that satisfies the first strategy capacity constraint is established, wherein the first strategy capacity constraint refers to the minimum energy storage capacity or discharge capacity that the energy storage system needs to ensure when performing the energy storage task; Based on the multi-feature SOC balance constraint, the first energy storage control strategy space is jointly optimized for multi-feature SOC balance, and a second energy storage control strategy space that satisfies the second strategy capacity constraint is established, wherein the multi-feature SOC balance constraint is used to measure the SOC balance degree between energy storage units, and on this basis, various characteristics of the charging and discharging process are comprehensively evaluated; Based on the SOC equilibrium fitness function, the SOC equilibrium fitness of the second energy storage control strategy space is optimized to obtain an energy storage control strategy optimization result; The energy storage control strategy optimization result is encrypted and sent to the DC microgrid energy storage system, and the DC microgrid energy storage system performs the energy storage operation task according to the energy storage control strategy optimization result; The multi-feature SOC balance joint optimization is performed on the first energy storage control strategy space based on the multi-feature SOC balance constraint to establish a second energy storage control strategy space that satisfies the second strategy capacity constraint, including: The multi-feature SOC balance constraint includes an SOC balance stability feature constraint, an SOC balance response feature constraint, and an SOC balance loss feature constraint, wherein the SOC balance stability feature constraint refers to the range of the SOC that the energy storage unit needs to maintain during the entire charging and discharging process; the SOC balance response feature constraint is used to evaluate the response speed of the energy storage unit in the energy storage task; and the SOC balance loss feature constraint is used to evaluate the energy loss of each energy storage unit during the charging and discharging process; Extracting an nth energy storage control strategy according to the first energy storage control strategy space, where n is a positive integer; Performing simulation control on the DC microgrid energy storage system according to the nth energy storage control strategy to obtain an nth energy storage simulation SOC state data set, wherein the nth energy storage simulation SOC state data set includes a plurality of energy storage simulation SOC state data corresponding to the plurality of energy storage units; Performing a multi-feature SOC balance evaluation based on the nth energy storage simulation SOC state data set to obtain an nth strategy SOC balance evaluation result; Determining whether the nth strategy SOC balance evaluation result satisfies the multi-feature SOC balance constraint; If the SOC balance evaluation result of the nth strategy satisfies the multi-feature SOC balance constraint, the nth energy storage control strategy is recorded as the nth energy storage control optimization strategy, and the nth energy storage control optimization strategy is added to the second energy storage control strategy space; Continue to perform multi-feature SOC balance joint optimization on the first energy storage control strategy space according to the multi-feature SOC balance constraint until the optimization strategy capacity of the second energy storage control strategy space satisfies the second strategy capacity constraint; The SOC equilibrium fitness function is: ； Among them, SOCF represents the SOC equilibrium fitness, exp represents the exponential function with the natural constant e as the base, g(SOCS) represents the normalized energy storage control SOC equilibrium stability evaluation coefficient, g(SOCA) represents the normalized energy storage control SOC equilibrium response evaluation coefficient, g(SOCL) represents the normalized energy storage control SOC equilibrium loss evaluation coefficient, α, β, γ represent the SOC equilibrium fitness calculation weights.

2. The method according to claim 1, characterized in that Initializing SOC balance analysis on the multiple energy storage units, and establishing energy storage operation priority constraints in combination with the energy storage task attributes, including: Collecting real-time SOC parameters corresponding to the multiple energy storage units to obtain SOC information of the multiple units; Performing pairwise deviation evaluation based on the plurality of unit SOC information to obtain a plurality of SOC deviation coefficients; Performing concentrated value calculation according to the plurality of SOC deviation coefficients to generate an SOC deviation balance coefficient; Determining whether the SOC deviation equilibrium coefficient is greater than / equal to an SOC deviation equilibrium threshold; If the SOC deviation balance coefficient is greater than / equal to the SOC deviation balance threshold, based on the energy storage task attributes, the multiple energy storage units are prioritized for energy storage scheduling according to the multiple unit SOC information to generate the energy storage operation priority constraint.

3. The method according to claim 1, characterized in that Based on the energy storage operation priority constraint, an energy storage control decision is made on the DC microgrid energy storage system according to the energy storage task attribute, the expected energy storage capacity and the expected energy storage duration, and a first energy storage control strategy space that satisfies the first strategy capacity constraint is established, including: Based on the energy storage task attributes, collecting energy storage control constraint information of the multiple energy storage units to obtain multiple energy storage control constraint domains; Based on the energy storage operation priority constraint, energy storage control decisions are made on the multiple energy storage units according to the multiple energy storage control constraint domains to obtain an energy storage control scheme space, wherein the energy storage control scheme space includes multiple energy storage control schemes, and each energy storage control scheme includes multiple energy storage control decisions corresponding to the multiple energy storage units; The expected energy storage capacity and the expected energy storage duration are used as energy storage control cleaning constraints, and feature cleaning is performed on the energy storage control solution space to obtain the first energy storage control strategy space that meets the first strategy capacity constraint.

4. The method according to claim 3, characterized in that Taking the expected energy storage capacity and the expected energy storage duration as energy storage control cleaning constraints, feature cleaning is performed on the energy storage control solution space to obtain the first energy storage control strategy space that satisfies the first strategy capacity constraint, including: Extracting a first energy storage control scheme according to the energy storage control scheme space; Based on the first energy storage control scheme, an energy storage capacity prediction is performed on the DC microgrid energy storage system to obtain a first predicted energy storage capacity; Based on the first energy storage control scheme, the DC microgrid energy storage system is predicted to have a first predicted energy storage duration; Determine whether the first predicted energy storage capacity is greater than / equal to the expected energy storage capacity, and determine whether the first predicted energy storage duration is less than / equal to the expected energy storage duration; If the first predicted energy storage capacity is greater than / equal to the expected energy storage capacity, and the first predicted energy storage duration is less than / equal to the expected energy storage duration, the first energy storage control scheme is recorded as a first energy storage control strategy, and the first energy storage control strategy is added to the first energy storage control strategy space; Based on the energy storage control cleaning constraint, continue to perform feature cleaning on the energy storage control solution space until the policy capacity of the first energy storage control policy space meets the first policy capacity constraint.

5. The method according to claim 1, characterized in that A multi-feature SOC balance evaluation is performed based on the nth energy storage simulation SOC state data set to obtain an nth strategy SOC balance evaluation result, including: Connecting the DC microgrid energy storage system, loading a sample energy storage control SOC state data set, a sample energy storage control SOC balance stability evaluation data set, a sample energy storage control SOC balance response evaluation data set, and a sample energy storage control SOC balance loss evaluation data set; Taking the sample energy storage control SOC state data set as input data and the sample energy storage control SOC equilibrium stability evaluation data set as output data, training the BP neural network to construct a SOC equilibrium stability feature evaluation model; Training a SOC equilibrium response characteristic evaluation model according to the sample energy storage control SOC state data set and the sample energy storage control SOC equilibrium response evaluation data set; Training a SOC balance loss feature evaluation model according to the sample energy storage control SOC state data set and the sample energy storage control SOC balance loss evaluation data set; Connecting the SOC equilibrium stability characteristic evaluation model, the SOC equilibrium response characteristic evaluation model and the SOC equilibrium loss characteristic evaluation model to generate a multi-characteristic SOC equilibrium evaluation model; Based on the nth energy storage simulation SOC state data set and according to the multi-feature SOC balance evaluation model, the nth strategy SOC balance evaluation result is output.

6. The method according to claim 1, characterized in that The SOC equilibrium fitness function is used to optimize the second energy storage control strategy space to obtain an energy storage control strategy optimization result, including: Extracting a first energy storage control optimization strategy according to the second energy storage control strategy space; Based on the first energy storage control optimization strategy, and according to the SOC equilibrium fitness function, calculating a first SOC equilibrium fitness; Extracting a second energy storage control optimization strategy according to the second energy storage control strategy space; Based on the second energy storage control optimization strategy, and according to the SOC equilibrium fitness function, calculating a second SOC equilibrium fitness; Based on the first SOC equilibrium fitness and the second SOC equilibrium fitness, optimizing the first energy storage control optimization strategy and the second energy storage control optimization strategy for the current SOC equilibrium fitness, and determining the current winning SOC equilibrium fitness and the current winning energy storage control strategy; Based on the SOC equilibrium fitness function and the current winning SOC equilibrium fitness, the current winning energy storage control strategy continues to be iteratively optimized until the number of iterative optimizations meets the iterative optimization number threshold, and the energy storage control strategy optimization result is generated.

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