Coordinated control methods, devices and electronic equipment for flow battery energy storage systems

Abstract

This specification provides a coordinated control method, device, and electronic equipment for a flow battery energy storage system. Based on this method, the state of charge (SOC) and state of health (SOH) values ​​of the flow battery energy storage units are simultaneously acquired. Then, by jointly considering the SOH values ​​of each flow battery energy storage unit and the differences in SOC values ​​between different units, a corresponding improved droop coefficient is determined. Based on the improved droop coefficient, targeted adjustments and controls are made to the flow battery energy storage units in the system. This allows for better adaptation to complex flow battery energy storage systems, enabling precise and efficient coordinated control of the operation of each flow battery energy storage unit. While ensuring the overall stability of the system, it also provides differentiated protection for flow battery energy storage units in different states, thereby improving the overall service life of the system.

Description

Technical Field

[0001] This specification belongs to the field of flow battery control technology, and in particular relates to coordinated control methods, devices and electronic equipment for flow battery energy storage systems. Background Technology

[0002] Flow batteries, with their advantages of long lifespan, large capacity, high safety, and flexible system design, have begun to be used to build large-capacity energy storage systems.

[0003] However, based on existing methods, it is often difficult to accurately and reasonably achieve power distribution among different flow batteries when performing operation control for complex energy storage systems containing multiple flow batteries. This can easily lead to one or more flow batteries failing to operate normally due to long-term overload aging, affecting the overall operational stability of the system.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This specification provides a coordinated control method, device, and electronic equipment for a flow battery energy storage system, which can accurately and efficiently coordinate and control the operation of each flow battery energy storage unit in the system, so as to ensure the overall stable operation of the system while achieving differentiated protection for flow battery energy storage units in different states of the system.

[0006] This specification provides a coordinated control method for a flow battery energy storage system, applicable to a flow battery energy storage system comprising at least: multiple flow battery energy storage units, wherein the multiple flow battery energy storage units are connected in parallel to a common DC bus via a bidirectional DC / DC converter, and the method includes: When the preset triggering conditions are met, the health status and state of charge values ​​of multiple flow battery energy storage units are collected. The average state of charge (SOC) of the system is determined based on the SOC values ​​of multiple flow battery energy storage units; and the SOC difference of the multiple flow battery energy storage units is calculated based on the SOC values ​​of multiple flow battery energy storage units and the average SOC of the system. Based on the differences in state of charge and health status of multiple flow battery energy storage units, the improved droop coefficients of multiple flow battery energy storage units are determined. The output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system are adjusted based on the improved droop factor of multiple flow battery energy storage units.

[0007] In one embodiment, determining the improved droop coefficient of the multiple flow battery energy storage units based on the difference in state of charge and the state of health of the multiple flow battery energy storage units includes: Acquire the current time period's status parameter monitoring records of the flow battery energy storage system; Based on the status parameter monitoring records for the current time period, determine the system power fluctuation parameters and the system state of charge mean fluctuation parameters for the current time period; Based on the preset mapping relationship, the influence intensity parameters that match the system power fluctuation parameters and the system state of charge mean fluctuation parameters for the current time period are determined. Based on the influence intensity parameter, the state of charge difference value of multiple flow battery energy storage units, and the health status value, the improved droop coefficient of multiple flow battery energy storage units is determined.

[0008] In one embodiment, determining the improved droop coefficient of the multiple flow battery energy storage units based on the influence intensity parameter, the state-of-charge difference value of the multiple flow battery energy storage units, and the health state value includes: The droop factor for the current improved flow battery energy storage unit is determined using the following formula:

[0009] in, This represents the improved droop factor for current flow battery energy storage units. This is the initial droop coefficient. SOH This represents the current health status value of the flow battery energy storage unit. SOC This represents the current state of charge (SOC) value of the flow battery energy storage unit. n To influence the strength parameters, This represents the difference in state of charge of the current flow battery energy storage unit.

[0010] In one embodiment, adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on an improved droop coefficient of the plurality of flow battery energy storage units includes: Obtain the wire type, temperature parameters, and heat dissipation coefficient of multiple flow battery energy storage units; Based on the conductor type, temperature parameters, and heat dissipation coefficient of multiple flow battery energy storage units, the line impedance of multiple flow battery energy storage units is determined. Based on the improved droop coefficient and line impedance of multiple flow battery energy storage units, a matching target regulation ratio is determined. Adjust the output current and / or output voltage of the flow battery energy storage unit in the flow battery energy storage system according to the target adjustment ratio.

[0011] In one embodiment, when the flow battery energy storage system includes at least a first flow battery energy storage unit and a second flow battery energy storage unit, determining the matching target adjustment ratio based on the improved droop coefficient and line impedance of the plurality of flow battery energy storage units includes: The target adjustment ratio is determined according to the following formula:

[0012] in, The target adjustment ratio is based on the current. This is the output current of the first flow battery energy storage unit. This is the output current of the second flow battery energy storage unit. The improved droop factor for the first flow battery energy storage unit. The improved droop factor for the second flow battery energy storage unit. The line impedance of the first flow battery energy storage unit is given. The line impedance is the second flow battery energy storage unit.

[0013] In one embodiment, adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of the plurality of flow battery energy storage units further includes: Obtain the current output voltage of multiple flow battery energy storage units; The reference voltage of multiple flow battery energy storage units is calculated based on the improved droop factor of multiple flow battery energy storage units. Based on the current output voltage and reference voltage of multiple flow battery energy storage units, determine the first voltage compensation signal for multiple flow battery energy storage units; The output voltage of the flow battery energy storage unit in the flow battery energy storage system is adjusted based on the first voltage compensation signal of multiple flow battery energy storage units.

[0014] In one embodiment, after adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system according to an improved droop coefficient of the plurality of flow battery energy storage units, the method further includes: Get the current voltage of the common DC bus; The second voltage compensation signal is determined based on the current voltage of the common DC bus and the preset voltage compensation reference value. The output voltage of the flow battery energy storage unit in the flow battery energy storage system is adjusted according to the second voltage compensation signal.

[0015] In one embodiment, the flow battery energy storage unit includes an energy storage unit based on a vanadium redox flow battery.

[0016] This specification also provides a coordination control device for a flow battery energy storage system, applied to a flow battery energy storage system, wherein the flow battery energy storage system includes at least: multiple flow battery energy storage units, the multiple flow battery energy storage units being connected in parallel to a common DC bus via a bidirectional DC / DC converter, and the device includes: The acquisition module is used to acquire the health status and state of charge values ​​of multiple flow battery energy storage units when preset trigger conditions are met. The first determining module is used to determine the average state of charge of the system based on the state of charge values ​​of multiple flow battery energy storage units; and to calculate the state of charge difference value of multiple flow battery energy storage units based on the state of charge values ​​of multiple flow battery energy storage units and the average state of charge of the system. The second determining module is used to determine the improved droop coefficient of multiple flow battery energy storage units based on the difference in state of charge and the health status value of multiple flow battery energy storage units. An adjustment module is used to adjust the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on an improved droop coefficient of multiple flow battery energy storage units.

[0017] This specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the coordinated control method for the flow battery energy storage system.

[0018] This specification also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the coordinated control method for the flow battery energy storage system.

[0019] This specification also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the coordinated control method for the flow battery energy storage system.

[0020] Based on the coordinated control method, device, and electronic equipment for the flow battery energy storage system provided in this specification, when preset triggering conditions are met, the system acquires the health status and state of charge (SOC) values ​​of multiple flow battery energy storage units in the system; determines the average SOC of the system based on the SOC values ​​of the multiple flow battery energy storage units; calculates the SOC difference value of the multiple flow battery energy storage units based on the SOC values ​​of the multiple flow battery energy storage units and the average SOC of the system; determines the improved droop coefficient of the multiple flow battery energy storage units based on the SOC difference value and the health status value; and adjusts the output current and / or output voltage of the flow battery energy storage units in the system based on the improved droop coefficient of the multiple flow battery energy storage units. First, the state of charge (SOC) and state of health (SOH) values ​​of each flow battery energy storage unit are simultaneously collected. Then, considering the SOH values ​​of each unit and the differences in SOC values ​​between them, and taking into account the specific state of each unit, an improved droop coefficient is accurately determined. Next, the flow battery energy storage units in the system are adjusted and controlled accordingly based on the improved droop coefficient. This allows for better adaptation to complex flow battery energy storage systems, enabling precise and efficient coordination and control of the operation of each unit. This ensures the overall stability of the flow battery energy storage system while providing refined and differentiated protection for units in different states, thereby improving the overall lifespan of the system. Attached Figure Description

[0021] To more clearly illustrate the embodiments of this specification, the accompanying drawings used in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a coordinated control method for a flow battery energy storage system provided in one embodiment of this specification. Figure 2 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 3 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 4 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 5 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 6 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 7 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 8 This is a schematic diagram of the structural composition of an electronic device provided in one embodiment of this specification; Figure 9 This is a schematic diagram of the structural composition of the coordination control device for a flow battery energy storage system provided in one embodiment of this specification; Figure 10 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 11 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 12 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 13 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 14 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 15 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 16 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 17 This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 18This is a schematic diagram of an embodiment of the coordinated control method for a flow battery energy storage system provided in this specification, applied in a scenario example. Figure 19 This is a schematic diagram of one embodiment of the coordinated control method for a flow battery energy storage system provided in the embodiments of this specification, applied in a scenario example. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0024] It should be noted that the information and data related to users involved in the embodiments of this specification are all information and data authorized by the user or fully authorized by the relevant parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with relevant laws, regulations, and standards, and necessary confidentiality measures have been taken. They do not violate public order and good morals, and corresponding operation entry points are provided for users or relevant parties to choose to authorize or refuse.

[0025] It should also be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0026] See Figure 1 As shown in the embodiments of this specification, a coordinated control method for a flow battery energy storage system is provided. This method is specifically applied to a flow battery energy storage system, which includes at least multiple flow battery energy storage units connected in parallel to a common DC bus via a bidirectional DC / DC converter. In specific implementations, the method may include the following: S101: When the preset triggering conditions are met, collect the health status and state of charge values ​​of multiple flow battery energy storage units. S102: Determine the average state of charge of the system based on the state of charge values ​​of multiple flow battery energy storage units; and calculate the state of charge difference value of multiple flow battery energy storage units based on the state of charge values ​​of multiple flow battery energy storage units and the average state of charge of the system. S103: Based on the differences in state of charge and health status of multiple flow battery energy storage units, determine the improved droop coefficient of multiple flow battery energy storage units. S104: Adjust the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of multiple flow battery energy storage units.

[0027] Specifically, the aforementioned flow battery can be understood as a type of storage battery, consisting of a stack unit, electrolyte, electrolyte storage and supply unit, and management and control unit. It is a high-performance storage battery that utilizes separate positive and negative electrolytes for independent circulation, and features high capacity, wide range of applications, and long cycle life.

[0028] Specifically, the aforementioned flow batteries may include vanadium redox flow batteries, etc.

[0029] It should be noted that the aforementioned flow batteries differ fundamentally from traditional closed electrochemical batteries (such as lithium-ion batteries, or lithium batteries) in terms of battery structure and working principle. This means that existing relatively mature methods for predicting the health status of lithium batteries are often not directly applicable to the treatment of flow batteries.

[0030] Specifically, as an open-type battery system, the aforementioned flow battery stores its active materials in an electrolyte within an external tank. Energy storage and release are achieved through the circulation of the electrolyte within the battery stack. This characteristic means that the battery's health is not only affected by the number of electrochemical cycles but also closely coupled with the stability of the electrolyte, the integrity of the ion-conducting membrane, and the system's operating conditions (e.g., electrolyte flow rate and temperature). For example, the capacity decay of vanadium redox flow batteries is mostly due to vanadium ion cross-contamination, electrolyte imbalance, and electrode and membrane degradation.

[0031] Furthermore, research revealed that the aforementioned degradation process strongly depends on the dynamic coupling of multiple operating parameters such as flow rate, temperature, and current. Conventional SOH prediction models for batteries, especially those designed for lithium-ion batteries, are mostly unable to effectively capture and model the complex nonlinear relationship between the specific operating parameter "flow rate" and electrical parameters; they also cannot fully characterize the complex time-series patterns dominated by the unique degradation mechanism of flow batteries, which exhibit both long-term degradation trends and short-term operating condition fluctuations. This results in insufficient prediction accuracy and robustness when applied to flow batteries, making them prone to errors during monitoring.

[0032] The aforementioned State of Health (SOH, or battery health status) can be understood as an indicator parameter that reflects the health status of battery performance, and is mainly used to assess the degree of battery performance degradation.

[0033] The aforementioned State of Charge (SOC) can be understood as a parameter used to measure the ratio of the battery's remaining usable capacity to its total capacity when fully charged.

[0034] See Figure 2 As shown, the aforementioned flow battery energy storage system includes at least a plurality of flow battery energy storage units, such as a first flow battery energy storage unit, ... an nth flow battery energy storage unit, etc. The plurality of flow battery energy storage units includes at least two flow battery energy storage units, such as a first flow battery energy storage unit and a second flow battery energy storage unit.

[0035] Specifically, each of the above-mentioned flow battery energy storage units includes at least: an energy storage module based on a flow battery, and a bidirectional DC / DC converter.

[0036] The aforementioned flow battery-based energy storage module is used to store and release electrical energy in conjunction with the system, thereby enhancing the overall stability of the system operation. The aforementioned bidirectional DC / DC converter serves as the connection point between the common DC bus and the flow battery energy storage unit, primarily used to achieve bidirectional energy conversion between the two.

[0037] The aforementioned energy storage modules based on flow batteries may specifically include energy storage modules based on vanadium redox flow batteries, such as VFB (Vanadium Flow Battery) energy storage modules.

[0038] Taking the VFB energy storage module as an example, and combining the structural and mechanistic characteristics of the vanadium redox flow battery, while comprehensively considering the losses during the electrochemical process and the dynamic response of the battery, an equivalent circuit loss model of the VFB energy storage module during the operation of the flow battery energy storage unit can be constructed. For details, please refer to [reference needed]. Figure 3 As shown. Subsequently, based on the equivalent circuit of the VFB energy storage module described above, combined with the circuit structure of the bidirectional DC / DC converter and the connection relationship between the bidirectional DC / DC converter and the common DC bus, equivalent circuits for each flow battery energy storage unit can be constructed.

[0039] In the above equivalent circuit loss model Indicates the internal resistance of the reaction. Indicates the internal resistance of the ohm. Indicates bypass internal resistance. Indicates the charging and discharging current. This represents the stack current inside the fuel cell stack. Represents capacitor current. Indicates pump loss current. This indicates the terminal voltage of the VFB energy storage module. This indicates the internal fuel cell voltage. This represents the capacitor voltage across the equivalent electrode capacitor, where C represents the equivalent electrode capacitance. Based on the above equivalent circuit loss model and the specific characteristics of the flow battery, the specific current-voltage relationship of the flow battery energy storage unit will be derived later.

[0040] The aforementioned bidirectional DC / DC converter (which can be abbreviated as DC / DC) can be understood as a DC-DC conversion device based on a bidirectional Buck / Boost topology that allows for bidirectional energy flow. Based on this bidirectional DC / DC converter, by controlling the on and off states of switching devices (e.g., MOSFETs / IGBTs) and in conjunction with energy storage components such as inductors and capacitors, voltage increases / decreases and flexible energy direction conversion can be achieved.

[0041] Compared to conventional conversion connection devices, the aforementioned bidirectional DC / DC converter not only has a simple structure and fewer component requirements, but also features low power loss and relatively mature control technology.

[0042] Specifically, each flow battery energy storage unit can be connected in parallel to a common DC bus via its own bidirectional DC / DC converter. This common DC bus can also be connected to an AC power grid via a corresponding converter (e.g., a DC / AC converter). Furthermore, the common DC bus can also be connected to corresponding DC loads. Specifically, the common DC bus can be the DC bus in a DC microgrid.

[0043] Furthermore, the aforementioned flow battery energy storage unit may also be equipped with a battery monitoring module (e.g., BMU, Battery Management Unit) and a temperature sensor corresponding to each flow battery energy storage unit. The battery monitoring module is used to collect and acquire the state parameters (including state of health and state of charge values) of its respective flow battery energy storage unit; the temperature sensor is used to monitor the temperature of the flow battery energy storage unit.

[0044] In specific implementation, for each flow battery energy storage unit, the battery monitoring module can monitor the health status value of the flow battery energy storage unit in the following manner: Obtain the voltage parameters of the flow battery energy storage unit in the current time period (e.g., the most recent 10 minutes); perform time alignment on the voltage parameters of the current time period to obtain time-aligned voltage parameters; calculate multiple key feature data of the current time period based on the time-aligned voltage parameters; according to preset splicing rules, splice the multiple key feature data of the current time period in chronological order to obtain the first key feature time sequence of the current time period; and use a preset battery health status prediction model to process the first key feature time sequence of the current time period to determine the current health status value of the flow battery energy storage unit.

[0045] The aforementioned preset battery health state prediction model is a hybrid model based on a Transformer structure and a bidirectional GRU structure. This preset battery health state prediction model, based on the aforementioned structure, can effectively balance the global time-series information and local dynamic information of the flow battery energy storage unit during operation, and exhibits good adaptability and sensitivity to changes in operating conditions.

[0046] Specifically, the aforementioned preset battery health state prediction model may include at least: a first feature processing module, a second feature processing module, and a regression calculation module connected in sequence; wherein, the first feature processing module is a module based on a Transformer structure, the second feature processing module is a module based on a bidirectional GRU structure, and the regression calculation module is a module based on a fully connected layer structure; the first feature processing module includes at least a feedforward neural network based on a multi-head attention mechanism; and the second feature processing module includes at least a parallel forward GRU structure and a reverse GRU structure.

[0047] The Transformer structure described above can be understood as a deep learning model architecture. This architecture primarily replaces the traditional Recurrent Neural Network (RNN) with a self-attention mechanism, and its core consists of encoder and decoder components. By introducing the Transformer structure into a pre-defined battery health state prediction model, the model can acquire better global dependency capture capabilities. This allows for the analysis of the time-series information of the voltage parameters of the flow battery energy storage unit, enabling the determination of the global feature information of the flow battery energy storage unit in the long-term cycle from a holistic perspective.

[0048] The aforementioned bidirectional GRU (Bidirectional Gated Cyclic Unit) structure can be understood as an extension of a gated cyclic unit, capable of simultaneously acquiring and processing both forward and reverse information of a sequence. By introducing this bidirectional GRU structure into a pre-defined battery health state prediction model, on the one hand, the model can determine the long-term performance degradation trend hidden in the flow battery energy storage unit during long-term cycling from a holistic perspective, based on global feature information and combining historical and future interaction relationships; on the other hand, the model can capture the local dynamic feature information (e.g., short-term operating condition fluctuation characteristics) of the flow battery energy storage unit under specific operating conditions at each time point based on changes in operating parameters at several adjacent time points.

[0049] In this way, by integrating the Transformer structure and the bidirectional GRU structure into the preset battery health state model, it is possible to simultaneously acquire and, based on the global feature information, hidden long-term performance degradation trend, and local dynamic feature information of the flow battery energy storage unit, better adapt to the relatively complex internal structure and operating conditions of flow batteries, and comprehensively and accurately predict the battery health state of the flow battery energy storage unit.

[0050] The above-mentioned method utilizes a preset battery health state prediction model to determine the battery health state of a flow battery by processing the time series sequence of the first key feature of the current time period. Specifically, this can include the following: The first feature processing module in the preset battery health state prediction model processes the time series sequence of the first key feature of the current time period to obtain a corresponding global feature vector. The global feature vector includes multiple feature groups arranged in chronological order, with each feature group corresponding to a point in time within the current time period. The forward GRU structure in the second feature processing module performs forward correlation processing on each feature group in the global feature vector to obtain forward hidden state information for each feature group. The reverse GRU structure in the second feature processing module performs reverse correlation processing on each feature group in the global feature vector to obtain reverse hidden state information for each feature group. The second feature processing module aggregates the forward and reverse hidden state information of each feature group to obtain comprehensive hidden state information for each feature group. The second feature processing module generates a corresponding comprehensive feature vector based on the comprehensive hidden state information of each feature group. The regression calculation module performs regression calculations based on the comprehensive feature vector to determine and output a corresponding second prediction result. The corresponding health state value is determined based on the second prediction result.

[0051] In specific implementation, for each flow battery energy storage unit, the battery monitoring module can monitor the state of charge (SOC) value of the flow battery energy storage unit in the following manner: Obtain the operating parameters of the flow battery energy storage unit for the current time period (e.g., the most recent 15 minutes); wherein the operating parameters include at least: voltage parameters, current parameters, and electrolyte flow rate parameters; according to preset feature processing rules, using the operating parameters of the flow battery energy storage unit for the current time period, determine the second key feature time sequence of the flow battery energy storage unit for the current time period; wherein the second key feature time sequence of the current time period contains multiple key feature groups arranged in chronological order; one key feature group corresponds to a point in time within the current time period; using a preset SOC prediction model to process the second key feature time sequence of the flow battery energy storage unit for the current time period, obtain the corresponding second prediction result; based on the second prediction result, determine the current SOC value of the flow battery energy storage unit.

[0052] The aforementioned preset state of charge prediction model includes at least an improved TCN network and an improved Informer network. The aforementioned operating parameters may also include temperature, electrolyte state parameters, etc.

[0053] The aforementioned TCN (Temporal Convolutional Network) can be understood as a type of convolutional network, suitable for processing long-term time series data and supporting parallel computing capabilities. Accordingly, introducing a TCN network into a pre-defined state of charge prediction model can, on the one hand, enable the model to support parallel computing and improve its processing efficiency; on the other hand, it can leverage the advantages of TCN networks in processing long-term time series data, facilitating subsequent long-range dependency modeling.

[0054] The Informer network mentioned above can be understood as a modified version of the Transformer network, which is suitable for processing more complex feature information.

[0055] The improved TCN network and the improved Informer network mentioned above can be understood as network structures adapted to flow battery detection after corresponding improvements to the TCN network and Informer network based on the structural characteristics of flow batteries and the relevant mechanistic characteristics during operation.

[0056] Specifically, the improved TCN network is a model network based on a global attention mechanism; the number of layers in the improved TCN network is greater than a preset reference number; the improved TCN network includes causal convolution and dilated convolution; the dilated convolution includes at least a first convolutional layer, a second convolutional layer, and a third convolutional layer; wherein, the first dilation coefficient of the first convolutional layer is less than the second dilation coefficient of the second convolutional layer, the second dilation coefficient of the second convolutional layer is less than the third dilation coefficient of the third convolutional layer; the third dilation coefficient is greater than a preset reference coefficient; the improved Informer network is a model network based on a sparse attention mechanism and a compression mechanism.

[0057] Furthermore, the aforementioned preset state of charge prediction model also includes: a feature mapping and sequence renormalization network, and a prediction network; the feature mapping and sequence renormalization network includes at least an average pooling layer and a fully connected network layer; wherein, the feature mapping and sequence renormalization network is used to connect the improved TCN network and the improved Informer network; the improved Informer network is connected to the prediction network.

[0058] It should be noted that, for flow battery energy storage units, considering the characteristics of slow system response and relatively smooth dynamic changes during operation, the conventional GRU sequence network was deliberately omitted when constructing the preset state of charge prediction model to adapt to its system inertia. Instead, the improved TCN network and the improved Informer network are directly used to collaboratively process the input data through the TCN-Informer structure.

[0059] In this way, by introducing and using a pre-defined state of charge prediction model that includes at least an improved TCN network and an improved Informer network, and omits the GRU network, the serial computation bottleneck of the GRU can be effectively eliminated while ensuring the model's prediction accuracy. This makes the model more suitable for long-sequence data on the operating characteristics of flow battery energy storage units, improves the overall processing efficiency, and meets the real-time detection requirements.

[0060] The above-mentioned method utilizes a preset state of charge prediction model to process the second key feature time series sequence of the flow battery energy storage unit in the current time period to obtain the corresponding second prediction result. Specifically, this can include the following: using causal convolution in the improved TCN network to process the second key feature time series sequence of the current time period to obtain historical causal information at each time point; using the first, second, and third convolutional layers in the improved TCN network to process the second key feature time series sequence of the current time period respectively to obtain first neighbor association information based on the first expansion coefficient, second neighbor association information based on the second expansion coefficient, and third neighbor association information based on the third expansion coefficient at each time point; using the improved TCN network to combine the historical causal information, the third neighbor association information, and the second neighbor association information at each time point to extract long-term time series trend change information of the flow battery energy storage unit in the current time period; and using the improved TCN network to combine the historical causal information and the first neighbor association information at each time point. The process involves extracting short-term dynamic change information for each time point within the current time period of the flow battery energy storage unit; fusing long-term trend change information and short-term dynamic change information within the current time period using an improved TCN network to obtain fused feature information for the flow battery energy storage unit; processing the fused feature information using a feature mapping and sequence renormalization network to obtain a high-order feature representation vector adapted to the improved Informer network; processing the high-order feature representation vector using the improved Informer network based on a sparse attention mechanism to determine the key high-order feature representation sequence corresponding to key time points; compressing the key high-order feature representation sequence using a compression mechanism to obtain a compressed key high-order feature representation sequence; performing global dependency modeling based on the compressed key high-order feature representation sequence using the improved Informer network to obtain the corresponding target key feature vector; and using a prediction network to perform regression calculations based on the target key feature vector to determine the corresponding second prediction result.

[0061] The aforementioned flow battery energy storage system may further include a controller (e.g., a voltage controller and / or a current controller) and a power monitor. The controller is connected to the bidirectional DC / DC converter of each flow battery energy storage unit and is used to adjust the power distribution of each flow battery energy storage unit by adjusting the output voltage and / or output current of each unit. The power monitor is used to monitor changes in system power.

[0062] Specifically, the controller can be connected to the power monitor, as well as the battery monitoring module and temperature sensor of each flow battery energy storage unit.

[0063] In specific implementation, when the time interval between the current monitoring time and the time since the last coordinated control of the flow battery energy storage system is greater than or equal to a preset time interval, it is determined that the preset trigger condition is met; or, when the power fluctuation amplitude of the system power of the flow battery energy storage system in the current time period is greater than a preset amplitude threshold, it is determined that the preset trigger condition is met; or, when the rate of decline of the health status values ​​of multiple flow battery energy storage units is greater than a preset risk rate threshold, it is determined that the preset trigger condition is met.

[0064] When the preset triggering conditions are met, the coordinated control method for the flow battery energy storage system provided in this manual can be triggered to perform the current coordinated control of the flow battery energy storage system.

[0065] In practice, the average state of charge (SOC) of the system can be calculated first based on the SOC values ​​of multiple flow battery energy storage units (which can be denoted as...). Then, calculate the difference between the state of charge (SOC) value of each flow battery energy storage unit and the average SOC value of the system to obtain the SOC difference value of each flow battery energy storage unit.

[0066] Specifically, the state-of-charge difference value of the flow battery energy storage unit numbered i can be calculated according to the following formula:

[0067] in, The state-of-charge difference value of the flow battery energy storage unit numbered i. Here is the state of charge (SOC) value for the flow battery energy storage unit numbered i. This represents the average state of charge of the system.

[0068] The state of charge difference value of the flow battery energy storage unit determined by the above method can be combined with the overall state of charge of the system to characterize the difference between the state of charge of a single flow battery energy storage unit and other flow battery energy storage units. It can simultaneously integrate global and local dimensions to accurately characterize the state of charge of the flow battery energy storage unit.

[0069] In practice, the improved droop coefficient of each flow battery energy storage unit can be determined based on the difference in state of charge and health status of multiple flow battery energy storage units.

[0070] Based on the improved droop coefficient determined by the above method, since both the health state and state of charge of the flow battery energy storage unit are considered, the degradation status and relative changes in state of charge of the flow battery energy storage unit can be comprehensively considered. This allows for more precise and reasonable coordination and adjustment of the operation of the flow battery energy storage unit. For individual flow battery energy storage units, this not only reduces losses and extends their service life but also avoids overcharging or over-discharging. For the flow battery energy storage system as a whole, based on the specific state and characteristics of different flow battery energy storage units, coordinated control improves the overall performance and efficiency of the system and extends its overall service life, resulting in better operational effects.

[0071] In practical implementation, a matching target control strategy can be determined based on the improved droop coefficients of multiple flow battery energy storage units. Then, based on this target control strategy, the output current of the corresponding flow battery energy storage unit in the flow battery energy storage system is adjusted (for example, it can be denoted as...). ) and / or output voltage (e.g., can be denoted as This enables voltage and / or current-based droop control to redistribute power within the flow battery energy storage unit.

[0072] The aforementioned droop control can be understood as a communication-free, distributed autonomous control strategy. Its core is to enable the flow battery energy storage converter (PCS) to simulate the "primary frequency regulation" characteristics of a synchronous generator, and to automatically adjust the active / reactive power through frequency or voltage deviation to achieve system stability and power balance among multiple units.

[0073] Specifically, for example, according to the target control strategy, for flow battery energy storage units with good health (e.g., health status value greater than a preset first health status threshold), their charging and discharging power is reduced by adjusting their output voltage and / or output current to reduce losses and extend their service life; for flow battery energy storage units with poor health (e.g., health status value less than or equal to a preset second health status threshold, which is less than the preset first health status threshold), their charging and discharging power is increased by adjusting their output voltage and / or output current to improve the overall performance and efficiency of the system; for flow battery energy storage units with normal health (e.g., health status value less than or equal to the preset first health status threshold and greater than the preset second health status threshold), their output voltage and / or output current are adjusted according to their state of charge to prevent overcharging or over-discharging, ensuring safe and stable operation of the flow battery energy storage unit.

[0074] Based on the above embodiments, the state of charge (SOC) and state of health (SOH) values ​​of the flow battery energy storage units are first collected simultaneously. Then, by jointly considering the SOH values ​​of each flow battery energy storage unit and the differences in SOC values ​​between different flow battery energy storage units, an improved droop coefficient is determined. Based on the improved droop coefficient, the flow battery energy storage units in the flow battery energy storage system are adjusted and controlled accordingly. This allows for better adaptation to complex flow battery energy storage systems, enabling precise and efficient coordination and control of the operation of each flow battery energy storage unit in the system. This ensures the overall stability of the system while providing differentiated protection for flow battery energy storage units in different states, thereby improving the overall service life of the system.

[0075] In some embodiments, the above-mentioned determination of the improved droop coefficient of multiple flow battery energy storage units based on the state of charge difference value and the health state value of multiple flow battery energy storage units may specifically include: adjusting the droop coefficient of the flow battery energy storage unit in a targeted manner when the health state value is large, and / or adjusting the droop coefficient of the flow battery energy storage unit in a targeted manner when the state of charge difference value is large, in order to obtain the improved droop coefficient of the flow battery energy storage unit.

[0076] In some embodiments, the influence intensity of the droop coefficient can be reasonably configured by further analyzing the overall power fluctuation and state of charge fluctuation of the system, and the corresponding influence intensity parameter (which can be denoted as n) can be obtained. Then, based on the influence intensity parameter, the state of charge difference value of multiple flow battery energy storage units, and the health status value, the improved droop coefficient of each flow battery energy storage unit in the multiple flow battery energy storage units can be determined.

[0077] In some embodiments, see Figure 4 As shown, the improved droop coefficient of multiple flow battery energy storage units is determined based on the differences in state of charge and health status values. In specific implementation, this may include the following: S4-1: Obtain the current time period's status parameter monitoring record of the flow battery energy storage system; S4-2: Based on the state parameter monitoring records for the current time period, determine the system power fluctuation parameters and the system state of charge mean fluctuation parameters for the current time period; S4-3: Based on the preset mapping relationship, determine the influence intensity parameters that match the system power fluctuation parameters and the system state of charge mean fluctuation parameters for the current time period; S4-4: Based on the influence intensity parameter, the state of charge difference value of multiple flow battery energy storage units, and the health status value, determine the improved droop coefficient of multiple flow battery energy storage units.

[0078] In practice, the current time period's status parameter monitoring records of the flow battery energy storage system can be obtained by querying the system operation log of the flow battery energy storage system.

[0079] The system operation log may include the system power of the flow battery energy storage system at multiple points in history, as well as the health status and state of charge values ​​of each flow battery energy storage unit in the system.

[0080] In practice, the system power of the flow battery energy storage system at multiple time points during the current time period, as well as the state of charge (SOC) values ​​of each flow battery energy storage unit at multiple time points during the current time period, can be obtained first based on the monitoring records of the state parameters during the current time period. Then, based on the system power of the flow battery energy storage system at multiple time points during the current time period, the fluctuation trend characteristics of the system power during the current time period can be determined through fitting calculations, which serve as the system power fluctuation parameters for the current time period. Simultaneously, based on the SOC values ​​of multiple flow battery energy storage units at each time point during the current time period, the average SOC value of the system at each time point during the current time period can be calculated. Then, based on the average SOC value of the system at each time point during the current time period, the fluctuation trend characteristics of the average SOC value of the system during the current time period can be determined through fitting calculations, which serve as the fluctuation parameters for the average SOC value of the system during the current time period.

[0081] In practice, the influence intensity parameters that match the system power fluctuation parameters and the average fluctuation parameters of the system state of charge in the current time period can be determined according to the preset mapping relationship.

[0082] Specifically, based on the preset mapping relationship, if the system power fluctuation parameters and / or the average fluctuation parameters of the system state of charge (SCC) are large in the current time period, it can be determined that the current system is in a stage of high power impact and / or large differences in SCC changes. In this case, according to the preset mapping relationship, a larger influence intensity parameter can be determined and used to enhance the sensitivity of the SCC difference value to the adjustment of the droop coefficient, accelerate the energy redistribution speed among the flow battery energy storage units, and thus shorten the SCC equalization time of the flow battery energy storage units. If the system power fluctuation parameters and the average fluctuation parameters of the system SCC are small in the current time period, it can be determined that the current system is in a stage of stable operation and / or small differences in SCC changes. In this case, according to the preset mapping relationship, a smaller influence intensity parameter can be determined and used to weaken the adjustment intensity of the SCC difference value to the droop coefficient, avoid excessive power fluctuations in the system, and thus improve the system's operational stability.

[0083] Specifically, the aforementioned pre-defined mapping relationship can be determined through data statistics and clustering learning based on test experimental records of a large number of sample systems.

[0084] In practice, for each flow battery energy storage unit, the state of charge (SCC) influence parameters can be determined first based on the SCC difference value and influence intensity parameters of the flow battery energy storage unit; then, based on the SCC influence parameters and the health status value, the improvement droop coefficient for the flow battery energy storage unit can be calculated.

[0085] In practical implementation, the target flow battery energy storage unit can be identified from among multiple flow battery energy storage units in the system based on the improved droop coefficients of multiple flow battery energy storage units. For example, the target flow battery energy storage unit can be identified as the flow battery energy storage unit whose absolute value of the difference between the improved droop coefficient and the preset reference droop coefficient is greater than a preset difference threshold. Then, based on the improved droop coefficients of multiple flow battery energy storage units, the target flow battery energy storage unit is subjected to voltage and / or current-matched droop control. Corresponding power redistribution is then performed on the target flow battery energy storage unit. This allows for full utilization of the target flow battery energy storage units with operational potential while ensuring the overall stability and performance of the system. Simultaneously, the power load of the target flow battery energy storage units with poor health or those that have been in a high-charge state for a long time is specifically reduced, thereby extending the service life of these target flow battery energy storage units and ultimately extending the overall service life of the system.

[0086] In practice, after determining the influence intensity parameters that match the system power fluctuation parameters and the average fluctuation parameters of the system state of charge in the current time period according to the preset mapping relationship, it is also possible to further obtain related data such as the energy storage capacity ratio of the flow battery energy storage unit, the cycle life of the flow battery energy storage unit, the power limitation parameters of the flow battery energy storage unit, and the intensity of system load fluctuation; then, the influence intensity parameters are adjusted and corrected according to the above-mentioned related data to obtain influence intensity parameters with relatively higher accuracy and better effect.

[0087] In some embodiments, the improved droop coefficient of the multiple flow battery energy storage units is determined based on the influence intensity parameter, the state of charge difference value of the multiple flow battery energy storage units, and the health state value. In specific implementations, this may include the following: The droop factor for the current improved flow battery energy storage unit is determined using the following formula:

[0088] in, This represents the improved droop factor for current flow battery energy storage units. This is the initial droop coefficient. SOH This represents the current health status value of the flow battery energy storage unit. SOC This represents the current state of charge (SOC) value of the flow battery energy storage unit. n To influence the strength parameters, This represents the difference in state of charge of the current flow battery energy storage unit.

[0089] Based on historical experience, the initial droop coefficient mentioned above can be set to 1.

[0090] Furthermore, the state-of-charge difference of the current flow battery energy storage unit can be calculated using the following formula:

[0091] in, is the current state of charge value of the flow battery energy storage unit, and i is the unit identifier of the current flow battery energy storage unit (e.g., the i-th flow battery energy storage unit). This represents the average state of charge of the system.

[0092] Using the above method, the improved droop coefficient of each flow battery energy storage unit in the flow battery energy storage system can be calculated separately.

[0093] In some embodiments, see Figure 5 As shown, the above-mentioned adjustment of the output current and / or output voltage of the flow battery energy storage unit in the flow battery energy storage system based on the improved droop coefficient of multiple flow battery energy storage units may, in specific implementation, include the following: S5-1: Obtain the wire type, temperature parameters, and heat dissipation coefficient of multiple flow battery energy storage units; S5-2: Determine the line impedance of multiple flow battery energy storage units based on the conductor type, temperature parameters, and heat dissipation coefficient of multiple flow battery energy storage units. S5-3: Determine the matching target regulation ratio based on the improved droop coefficient and line impedance of multiple flow battery energy storage units; S5-4: Adjust the output current and / or output voltage of the flow battery energy storage unit in the flow battery energy storage system according to the target adjustment ratio.

[0094] In practice, the initial line impedance can be determined first by acquiring and analyzing the conductor type and temperature parameters of the flow battery energy storage unit. Then, based on the temperature parameters at multiple time points within the current time period and the heat dissipation components of the flow battery energy storage unit, simulation can be used to determine the temperature parameter variation trend characteristics of the flow battery energy storage unit, considering the heat dissipation effect of the heat dissipation components, thus obtaining the corresponding heat dissipation coefficient. Finally, the initial line impedance can be corrected and adjusted based on this heat dissipation coefficient to obtain the line impedance of the flow battery energy storage unit (which can be denoted as...). ).

[0095] In practical implementation, a corresponding proportional regulation model can be constructed first based on the equivalent circuit of the flow battery energy storage unit and Kirchhoff's model. Then, using this proportional regulation model, a matching target regulation ratio for voltage and / or current can be determined based on the improved droop coefficient and line impedance of each flow battery energy storage unit. Subsequently, based on the target regulation ratio, the output current and / or output voltage of the corresponding flow battery energy storage unit can be adjusted to achieve matching droop control, thereby reasonably and accurately realizing the power redistribution of the corresponding flow battery energy storage unit.

[0096] Specifically, taking a flow battery energy storage system comprising a first flow battery energy storage unit and a second flow battery energy storage unit as an example, firstly, based on the equivalent circuit of a single flow battery energy storage unit, a system equivalent circuit can be constructed that includes at least two flow battery energy storage units connected in parallel: the first flow battery energy storage unit and the second flow battery energy storage unit. (See reference...) Figure 6 As shown. In the equivalent circuit diagram of this system, This is the output current of the first flow battery energy storage unit. This is the output current of the second flow battery energy storage unit. The improved droop factor for the first flow battery energy storage unit. The improved droop factor for the second flow battery energy storage unit. The line impedance of the first flow battery energy storage unit is given. The line impedance of the second flow battery energy storage unit is given. This is the output voltage of the first flow battery energy storage unit. This is the output voltage of the second flow battery energy storage unit. This is the reference value for the DC bus voltage of the first flow battery energy storage unit. This is the reference value for the DC bus voltage of the second flow battery energy storage unit. This is the measured voltage on the bus side. This is the equivalent impedance on the busbar side.

[0097] At the same time, the first relationship regarding droop control is determined, which can be specifically expressed as the following formula:

[0098] in, This represents a reference value (or reference voltage) for the output voltage of a flow battery energy storage unit based on droop control. This indicates the reference value of the DC bus voltage of the flow battery energy storage unit. An improved droop factor for flow battery energy storage units. This represents the output current of the flow battery energy storage unit. It should be noted that the first equation described above can be applied to any flow battery energy storage unit in a flow battery energy storage system.

[0099] Meanwhile, based on Kirchhoff's model and the equivalent circuit of the system, the following second relation can be established: .

[0100] Furthermore, considering that within this parallel-operational flow battery energy storage system, the DC bus voltage reference value of each parallel flow battery energy storage unit is equal, i.e., the following third relationship exists: .

[0101] Based on the third relation, the second relation is transformed to obtain the corresponding fourth relation: .

[0102] Based on the fourth relation, a corresponding proportional adjustment model can be constructed, for example... .

[0103] Furthermore, a proportional adjustment model can be used to determine a matching target adjustment ratio based on the improved droop coefficient and line impedance of the first and second flow battery energy storage units. Based on the target adjustment ratio, the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system can be adjusted accordingly by utilizing the bidirectional DC / DC converters of the first and second flow battery energy storage units, thereby achieving power redistribution between the first and second flow battery energy storage units.

[0104] In practical implementation, when the line impedance of the first flow battery energy storage unit and the line impedance of the second flow battery energy storage unit are relatively small, for example, much smaller than the order of magnitude of the improved droop coefficient, the proportional model can be further simplified and modified into the following form: .

[0105] In some embodiments, when the flow battery energy storage system includes at least a first flow battery energy storage unit and a second flow battery energy storage unit, the target adjustment ratio is determined based on the improved droop coefficient and line impedance of the plurality of flow battery energy storage units. In specific implementation, the target adjustment ratio can be determined according to the following formula:

[0106] in, The target adjustment ratio is based on the current. This is the output current of the first flow battery energy storage unit. This is the output current of the second flow battery energy storage unit. The improved droop factor for the first flow battery energy storage unit. The improved droop factor for the second flow battery energy storage unit. The line impedance of the first flow battery energy storage unit is given. The line impedance is the second flow battery energy storage unit.

[0107] In some embodiments, see Figure 7 As shown, the above-mentioned adjustment of the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of multiple flow battery energy storage units may, in specific implementations, also include the following: S7-1: Obtain the current output voltage of multiple flow battery energy storage units; S7-2: Calculate the reference voltage of multiple flow battery energy storage units based on the improved droop factor of multiple flow battery energy storage units; S7-3: Determine the first voltage compensation signal for the multiple flow battery energy storage units based on the current output voltage and reference voltage of the multiple flow battery energy storage units; S7-4: Adjust the output voltage of the flow battery energy storage unit in the flow battery energy storage system according to the first voltage compensation signal of multiple flow battery energy storage units.

[0108] In practical implementation, the reference voltage of each flow battery energy storage unit can be calculated based on the improved droop coefficient of each unit and the first relational formula for droop control. Then, the voltage difference between the current output voltage of each unit and the reference voltage is obtained through a subtraction operation. Finally, a voltage compensator is used to generate a first voltage compensation signal for that flow battery energy storage unit based on this voltage difference (which can be denoted as...). ).

[0109] In practice, a voltage controller can be used to adjust the output voltage of the corresponding flow battery energy storage unit according to the first voltage compensation signal.

[0110] After the output voltage adjustment is completed, a reference current regarding the inductor current can be further determined based on the voltage controller (which can be denoted as...). ), and obtain the current inductor current (which can be denoted as ). Then, calculate the current difference between the reference current and the current inductor current; using the current controller, adjust the switching on and off of the bidirectional DC / DC converter according to this current difference to control the duty cycle, thereby ensuring the overall accurate and efficient operation of the system.

[0111] In some embodiments, it is also considered that the adjustment process based on the above-mentioned droop control will cause a bus voltage drop, which in turn will affect the overall operation of the system. Therefore, it is further considered that a matching second voltage compensation signal can be determined by combining a preset voltage compensation reference value with a pre-configured fixed value; and then secondary compensation can be performed according to the second voltage compensation signal to reduce the bus voltage drop caused by the droop coefficient during the adjustment process, thereby effectively maintaining the voltage stability of the overall system operation.

[0112] In some embodiments, after adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system according to an improved droop coefficient of the plurality of flow battery energy storage units, the method may further include the following: S1: Get the current voltage of the common DC bus; S2: Determine the second voltage compensation signal based on the current voltage of the common DC bus and the preset voltage compensation reference value; S3: Adjust the output voltage of the flow battery energy storage unit in the flow battery energy storage system according to the second voltage compensation signal.

[0113] Specifically, the aforementioned preset voltage compensation reference value can be a fixed voltage value that has been pre-configured and determined based on the overall operating requirements parameters of the system and in conjunction with historical operating records.

[0114] In practice, the measured voltage on the bus side can be obtained. The current voltage of the common DC bus is used as the reference value; then the current voltage of the common DC bus is calculated and compared with the preset voltage compensation reference value (for example, it could be...). The difference between the two signals is used to determine the second voltage compensation signal; then, the voltage compensator can be used to determine the second voltage compensation signal based on a preset transfer function (which can be denoted as...). Using the second voltage compensation signal, the output voltage of the corresponding flow battery energy storage unit is adjusted so that the difference between the measured voltage on the bus side and the preset voltage compensation reference value is less than or equal to a preset deviation threshold. The preset deviation threshold can be a very small value.

[0115] Specifically, for example, before performing secondary compensation, for any flow battery energy storage unit, the following first state relationship is satisfied: .

[0116] After secondary compensation in the manner described above, for any flow battery energy storage unit, the second state relationship shown is satisfied: .

[0117] The preset transfer function satisfies the following relationship: .

[0118] Based on the above embodiments, by introducing and based on a preset voltage compensation reference value, secondary compensation is performed on the system adjusted based on droop control, which can effectively weaken or avoid the bus voltage drop caused by the droop coefficient, so that the bus side voltage can be stabilized near the expected value (e.g., the preset voltage compensation reference value), thereby ensuring the overall stable operation of the system.

[0119] In some embodiments, the flow battery energy storage unit may specifically include an energy storage unit based on a vanadium redox flow battery, etc.

[0120] As can be seen from the above, the coordinated control method for the flow battery energy storage system provided in the embodiments of this specification, when a preset triggering condition is met, collects the health status value and state of charge value of multiple flow battery energy storage units in the flow battery energy storage system; determines the average state of charge of the system based on the state of charge values ​​of multiple flow battery energy storage units; calculates the state of charge difference value of multiple flow battery energy storage units based on the state of charge values ​​of multiple flow battery energy storage units and the average state of charge of the system; then determines the improved droop coefficient of multiple flow battery energy storage units based on the state of charge difference value and the health status value; and adjusts the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of multiple flow battery energy storage units. First, the state of charge (SOC) and state of health (SOH) values ​​of the flow battery energy storage units are collected simultaneously. Then, by jointly considering the SOH values ​​of each flow battery energy storage unit and the differences in SOC values ​​between different flow battery energy storage units, an improved droop coefficient is determined. Based on the improved droop coefficient, the flow battery energy storage units in the flow battery energy storage system are adjusted and controlled accordingly. This allows for better adaptation to complex flow battery energy storage systems, enabling precise and efficient coordination and control of the operation of each flow battery energy storage unit in the system. This ensures the overall stability of the system while providing differentiated protection for flow battery energy storage units in different states, thereby improving the overall service life of the system.

[0121] This specification provides an electronic device through its embodiments. (See attached document.) Figure 8 As shown. The electronic device includes a network communication port 801, a processor 802, and a memory 803. These structures are connected by internal cables so that they can perform specific data interaction.

[0122] Specifically, the network communication port 801 can be used to collect the health status and state of charge values ​​of multiple flow battery energy storage units when a preset trigger condition is met.

[0123] The processor 802 can specifically be used to determine the average state of charge (SOC) of the system based on the SOC values ​​of multiple flow battery energy storage units; calculate the SOC difference value of the multiple flow battery energy storage units based on the SOC values ​​of the multiple flow battery energy storage units and the average SOC of the system; determine the improved droop coefficient of the multiple flow battery energy storage units based on the SOC difference value and the health status value; and adjust the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of the multiple flow battery energy storage units.

[0124] The memory 803 can be used to store the corresponding instruction program and related intermediate data.

[0125] Based on the above method, the relevant structural performance of electronic devices can be effectively utilized to improve the data processing speed of electronic devices and efficiently realize the data processing for coordinated control of flow battery energy storage systems.

[0126] In this embodiment, the network communication port 801 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0127] In this embodiment, the processor 802 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0128] In this embodiment, the memory 803 may include multiple layers. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0129] This specification also provides a computer-readable storage medium based on the above-described coordinated control method for a flow battery energy storage system. The computer-readable storage medium stores computer program instructions that, when executed, implement the following: when a preset trigger condition is met, acquire the health status values ​​and state of charge values ​​of multiple flow battery energy storage units; determine the average state of charge of the system based on the state of charge values ​​of the multiple flow battery energy storage units; calculate the state of charge difference value of the multiple flow battery energy storage units based on the state of charge values ​​of the multiple flow battery energy storage units and the average state of charge of the system; determine an improved droop coefficient for the multiple flow battery energy storage units based on the state of charge difference value and the health status values; and adjust the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of the multiple flow battery energy storage units.

[0130] In this embodiment, the storage medium includes, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured according to standards specified in the communication protocol for network connection communication.

[0131] In this embodiment, the specific functions and effects implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other embodiments, and will not be repeated here.

[0132] This specification also provides a computer program product, comprising at least a computer program, which, when executed by a processor, performs the following method steps: when a preset trigger condition is met, acquiring the health status value and state of charge value of multiple flow battery energy storage units; determining the average state of charge of the system based on the state of charge values ​​of the multiple flow battery energy storage units; calculating the state of charge difference value of the multiple flow battery energy storage units based on the state of charge values ​​of the multiple flow battery energy storage units and the average state of charge of the system; determining the improved droop coefficient of the multiple flow battery energy storage units based on the state of charge difference value and the health status value; and adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of the multiple flow battery energy storage units.

[0133] See Figure 9 As shown, at the software level, this specification also provides a coordination control device for a flow battery energy storage system. This device can be specifically applied to a flow battery energy storage system, which includes at least a plurality of flow battery energy storage units. These units are connected in parallel to a common DC bus via a bidirectional DC / DC converter. The device can specifically utilize the following structural modules: The acquisition module 901 can be used to acquire the health status and state of charge values ​​of multiple flow battery energy storage units when preset triggering conditions are met. The first determining module 902 can be used to determine the average state of charge of the system based on the state of charge values ​​of multiple flow battery energy storage units; and to calculate the state of charge difference value of multiple flow battery energy storage units based on the state of charge values ​​of multiple flow battery energy storage units and the average state of charge of the system. The second determining module 903 can be used to determine the improved droop coefficient of multiple flow battery energy storage units based on the difference in state of charge and health status of multiple flow battery energy storage units. The adjustment module 904 can be used to adjust the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of multiple flow battery energy storage units.

[0134] In some embodiments, when the second determining module 903 is specifically implemented, the improved droop coefficient of the multiple flow battery energy storage units can be determined in the following manner based on the state-of-charge difference value and health status value of the multiple flow battery energy storage units: acquiring the state parameter monitoring record of the flow battery energy storage system for the current time period; determining the system power fluctuation parameter and the system state-of-charge mean fluctuation parameter for the current time period based on the state parameter monitoring record for the current time period; determining the influence intensity parameter that matches the system power fluctuation parameter and the system state-of-charge mean fluctuation parameter for the current time period based on the preset mapping relationship; and determining the improved droop coefficient of the multiple flow battery energy storage units based on the influence intensity parameter, the state-of-charge difference value of the multiple flow battery energy storage units, and the health status value.

[0135] In some embodiments, when the second determining module 903 is specifically implemented, it can also determine the improved droop coefficient of the multiple flow battery energy storage units according to the influence intensity parameter, the state of charge difference value of the multiple flow battery energy storage units, and the health status value:

[0136] in, This represents the improved droop factor for current flow battery energy storage units. This is the initial droop coefficient. SOH This represents the current health status value of the flow battery energy storage unit. SOC This represents the current state of charge (SOC) value of the flow battery energy storage unit. n To influence the strength parameters, This represents the difference in state of charge of the current flow battery energy storage unit.

[0137] In some embodiments, when the adjustment module 904 is specifically implemented, it can adjust the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system according to the improved droop coefficient of the multiple flow battery energy storage units in the following manner: obtaining the wire type, temperature parameters, and heat dissipation coefficient of the multiple flow battery energy storage units; determining the line impedance of the multiple flow battery energy storage units based on the wire type, temperature parameters, and heat dissipation coefficient of the multiple flow battery energy storage units; determining a matching target adjustment ratio based on the improved droop coefficient and line impedance of the multiple flow battery energy storage units; and adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system according to the target adjustment ratio.

[0138] In some embodiments, when the flow battery energy storage system includes at least a first flow battery energy storage unit and a second flow battery energy storage unit, the adjustment module 904, in its specific implementation, can determine a matching target adjustment ratio based on the improved droop coefficient and line impedance of the multiple flow battery energy storage units in the following manner:

[0139] in, The target adjustment ratio is based on the current. This is the output current of the first flow battery energy storage unit. This is the output current of the second flow battery energy storage unit. The improved droop factor for the first flow battery energy storage unit. The improved droop factor for the second flow battery energy storage unit. The line impedance of the first flow battery energy storage unit is given. The line impedance is the second flow battery energy storage unit.

[0140] In some embodiments, when the adjustment module 904 is specifically implemented, it can also adjust the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system according to the improved droop coefficient of the multiple flow battery energy storage units in the following manner: obtaining the current output voltage of the multiple flow battery energy storage units; calculating the reference voltage of the multiple flow battery energy storage units according to the improved droop coefficient of the multiple flow battery energy storage units; determining the first voltage compensation signal of the multiple flow battery energy storage units according to the current output voltage and the reference voltage of the multiple flow battery energy storage units; and adjusting the output voltage of the flow battery energy storage units in the flow battery energy storage system according to the first voltage compensation signal of the multiple flow battery energy storage units.

[0141] In some embodiments, after adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system according to the improved droop coefficient of the plurality of flow battery energy storage units, the device may further be used to: obtain the current voltage of the common DC bus; determine a second voltage compensation signal according to the current voltage of the common DC bus and a preset voltage compensation reference value; and adjust the output voltage of the flow battery energy storage units in the flow battery energy storage system according to the second voltage compensation signal.

[0142] In some embodiments, the flow battery energy storage unit may specifically include an energy storage unit based on a vanadium redox flow battery.

[0143] It should be noted that the units, devices, or modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described by dividing them into various modules according to their functions. Of course, in implementing this specification, the functions of each module can be implemented in one or more software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection between the devices or units shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0144] As can be seen from the above, the coordinated control device for the flow battery energy storage system provided in the embodiments of this specification first simultaneously collects the state of charge (SOC) and state of health (SOH) values ​​of the flow battery energy storage units; then, by jointly considering the SOH values ​​of each flow battery energy storage unit and the differences in SOC values ​​between different flow battery energy storage units, an improved droop coefficient is determined; and the flow battery energy storage units in the flow battery energy storage system are adjusted and controlled accordingly based on the improved droop coefficient. This allows for better adaptation to complex flow battery energy storage systems, enabling precise and efficient coordinated control of the operation of each flow battery energy storage unit in the system. This ensures the overall stability of the system while providing differentiated protection for flow battery energy storage units in different states, thereby improving the overall service life of the system.

[0145] In a specific scenario example, the coordinated control method for flow battery energy storage systems provided in this manual can be applied to achieve parallel coordinated control of flow battery energy storage systems considering health conditions. For detailed implementation procedures, please refer to the following content.

[0146] In this scenario example, considering the advantages of vanadium redox flow batteries (VFBs) such as long lifespan, large capacity, high safety, and flexible system design, they are very suitable for building large-capacity energy storage systems. To meet the capacity requirements of microgrid systems and the power supply requirements of loads, VFBs are often connected to the DC microgrid via bidirectional DC / DC converters to form a parallel VFB system. The VFB energy storage module is mainly responsible for the storage and release of electrical energy, effectively enhancing the overall stability of the system. The bidirectional DC / DC converter serves as the connection point between the DC bus and the energy storage unit, realizing bidirectional energy conversion. Its parallel operation control strategy is crucial for the stable and efficient operation of the system. However, existing methods often simultaneously address three core issues: "high-reliability operation in environments without communication," "bus voltage drop caused by line impedance and droop characteristics," and "adaptive power allocation based on battery state of health (SOH)." Specifically, the existence of virtual impedance leads to a decrease in bus voltage; furthermore, since traditional droop control uses a fixed droop coefficient, while the operating states of each module in a microgrid are constantly changing and the line impedances are different, it is difficult to achieve dynamic power allocation.

[0147] To address the aforementioned issues and their root causes, this scenario example abandons reliance on real-time control communication and innovatively introduces an adaptive power-law droop coefficient based on State of Health (SOH) and State of Charge (SOC) into the local controller. This achieves autonomous allocation of power based on the SOH, allowing for higher output from high-capacity (high SOH) modules and lower output from low-capacity modules. Simultaneously, a localized secondary voltage compensation mechanism is designed to eliminate voltage deviations caused by droop effects locally using a fixed voltage reference. Considering the differences between energy storage modules, SOH and SOC are incorporated into the droop control coefficient, proposing a parallel coordinated control method for flow battery energy storage that considers the healthy state of the modules. A secondary compensation stage is also introduced to reduce bus voltage drop. Furthermore, a system simulation model is established based on the above approach, and simulation verification is performed on the Matlab / Simulink platform. The specific implementation may include the following steps.

[0148] 1. Several vanadium redox flow battery energy storage module units (e.g., flow battery energy storage units) are connected in parallel, and each module unit is connected in parallel to a common DC bus through a bidirectional DC / DC converter.

[0149] Furthermore, it should be noted that the vanadium redox flow battery energy storage unit consists of a VFB energy storage module and a bidirectional DC / DC converter. The VFB energy storage module is mainly responsible for the storage and release of electrical energy, effectively enhancing the overall stability of the system. The bidirectional DC / DC converter serves as the connection point between the DC bus and the energy storage unit, realizing bidirectional energy conversion. The structure of the vanadium redox flow battery energy storage system can be found in [reference needed]. Figure 2 As shown.

[0150] In vanadium redox flow battery (VFB) energy storage technology, the equivalent loss model plays a crucial role. The equivalent loss model visually and clearly displays the internal equivalent structure of the battery through resistance and capacitance, facilitating the analysis of its operating characteristics and accurately simulating losses during electrochemical processes and the battery's dynamic response. The equivalent loss circuit model of VFB can be found in [reference needed]. Figure 3 The equivalent loss circuit of VFB is shown. Wherein, Internal resistance to reaction, internal resistance For ohmic internal resistance, For bypass internal resistance, This is the charging and discharging current. This refers to the stack current inside the fuel cell stack. For capacitor current, For pump loss current, This is the terminal voltage of VFB. This refers to the internal fuel cell stack voltage. C is the voltage across the equivalent capacitor, and C is the electrode capacitance.

[0151] Furthermore, when designing a parallel DC energy storage system, a bidirectional DC converter is generally used to connect the energy storage system and the DC grid for bidirectional energy transfer. This bidirectional DC / DC converter adopts a bidirectional Buck / Boost topology to achieve effective energy interaction within the system. The bidirectional Buck / Boost converter not only has a simple structure and fewer component requirements, but also features low power loss and relatively mature control technology.

[0152] 2. Collect the State of Health (SOH) value and State of Charge (SOC) value of each parallel vanadium redox flow battery energy storage module.

[0153] In a parallel vanadium redox flow battery energy storage system, each energy storage module can be equipped with an independent BMU. The BMU is a battery monitoring unit that monitors and uploads the SOH and SOC values ​​to the central control system in real time.

[0154] 3. Combining the SOH value of each module with the differences in SOC between energy storage units, and taking into account the average SOC of each energy storage unit (e.g., the average state of charge of the system). Connect the current module SOC with Get by doing bad things By incorporating this into the droop coefficient, an improved droop control expression considering SOH is obtained as follows: Dynamic calculation of improved droop coefficient The calculation formula is as follows: ,in, , where n (e.g., the influence intensity parameter) is an adjustable exponent.

[0155] It should be further clarified that in existing traditional DC microgrid droop control technology, droop control generally refers to... Droop control, by adjusting the droop coefficient of the corresponding DC / DC converter, achieves current and voltage distribution. The droop control expression can be represented as: .in, This indicates the output voltage of the converter. express Reference value, This is the actual output current of the bidirectional DC-DC converter in the energy storage unit. is the reference value of the DC bus voltage of the DC microgrid, and k is the droop coefficient.

[0156] For example, taking a dual-branch parallel energy storage unit in a DC microgrid as an example, its equivalent circuit can be found in [reference needed]. Figure 6 As shown. It includes two parallel energy storage units. and It is the output voltage of the two energy storage unit converters. and These are the reference values ​​for the DC bus voltage of the two energy storage units, respectively. and These are the droop coefficients for the two units, respectively. , These are the line impedances of the two units, , This corresponds to the output current of the converter. The actual voltage on the bus side can be represented by the following formula according to Kirchhoff's laws: .

[0157] Under normal circumstances, within a parallel-operated system, the bus reference voltage of each parallel energy storage unit is equal, as shown in the following formula: .

[0158] Combining the above relationships, the ratio of the output currents of the two converters can be obtained as shown in the following formula: .

[0159] Specifically, when the line resistance of the DC microgrid system is ignored, the converter output current is only affected by the droop control coefficient. If the same droop control coefficient is used, i.e. At this point, we can obtain In other words, droop control achieves current sharing among different converters. The actual resistance of a DC microgrid system line is affected by factors such as conductor material, operating temperature, and environmental conditions. Therefore, in the modeling process, different conductor types can be distinguished by introducing material resistivity parameters, the temperature coefficient of resistance can be used to correct for the effect of temperature rise, and the equivalent resistance of the line can be adjusted in combination with environmental heat dissipation conditions to more comprehensively reflect the impact of line resistance changes on system operating characteristics. When considering line resistance, even if the same droop coefficient is used on different converters, it is difficult to achieve a complete balance. In other words, the presence of line resistance will affect the distribution of current.

[0160] Based on existing traditional DC microgrid droop control technology, this application proposes an improved droop control considering State of Health (SOH), as follows: When formulating battery charging and discharging strategies, the health status of each energy storage unit is considered. For units with low SOH, lower charging and discharging power is used to reduce losses and extend service life. For units with good health, the charging and discharging power can be appropriately increased to improve the overall system performance and efficiency. When charging and discharging energy storage units with good health, their own State of Charge (SOC) also needs to be considered to prevent overcharging or over-discharging. Combining the SOH value of each module and considering the differences in SOC between energy storage units, and combining the average SOC of each energy storage unit, the difference between the current module's SOC and the average SOC is used to obtain ΔSOC, which is then introduced into the droop coefficient, resulting in the improved droop control expression considering SOH, as shown in the following equation: .

[0161] The improved sag coefficient and related parameters are shown below: , .

[0162] In the above formula, the initial droop control coefficient Based on this, the capacity difference between SOH and SOC of the energy storage unit itself is introduced. The health status of the energy storage unit has a decisive impact on the charging and discharging behavior and the overall performance of the system. As SOH decreases, the droop coefficient must be adjusted to address the problem of accelerated SOC change during the discharge and charging process of battery packs with reduced SOH. This adjustment can effectively control the output power and charging power, thereby achieving the goal of reducing the rate of SOC change.

[0163] Furthermore, it should be noted that the power exponent n plays a crucial role, determining the strength of the influence of ΔSOC on the droop coefficient, thus directly affecting the efficiency and speed of energy distribution in the system. Parallel energy storage systems using this strategy can adjust the value of n; when the system is under high-power surges or significant SOC differences, the value of n can be appropriately increased to enhance [the system's performance]. The sensitivity to droop coefficient adjustment accelerates the energy redistribution speed among energy storage units and shortens the SOC equalization time. When the system is in steady-state operation or the SOC difference is small, the value of n can be appropriately reduced to decrease the adjustment intensity, avoid excessive power fluctuations, and improve system stability. In scenarios with large differences in energy storage capacity or different aging levels, factors such as capacity ratio, cycle life, and power limitations need to be comprehensively considered to coordinate the setting of n, balancing equalization efficiency and equipment safety. Therefore, the adjustment of n should comprehensively consider the degree of SOC deviation, the intensity of load fluctuation, the matching of energy storage capacity, and system stability requirements, achieving a trade-off between response speed and operational stability. It can flexibly control the slope of the droop curve and power difference to achieve faster or slower system equalization, thereby optimizing the overall battery system's operating performance and efficiency.

[0164] In practical implementation, the DC-side parallel energy storage system calculates the average SOC of the energy storage system based on the current SOC of each energy storage unit, and then further calculates the SOC using relevant formulas. The SOH of each unit and Substituting into the relevant formula, we get Voltage reference Combined with the compensation signal of the voltage compensator Compare it with the actual output voltage The difference is transmitted to the voltage controller, which then generates an inductor current reference. Its relationship with the actual inductor current The difference is sent to the current controller for processing. By adjusting the on and off states of the switching transistor, the duty cycle d is controlled, thereby ensuring the accurate and efficient operation of the system.

[0165] 4. To address the bus voltage drop issue during the droop adjustment process, an improved droop method considering SOH (State of Harshness) is implemented, introducing secondary compensation and setting a fixed voltage compensation reference value. Set the actual side voltage of the DC bus. ,use and The deviation between them, Adjust the bus voltage Stable at nearby.

[0166] Specifically, this involves setting a fixed voltage compensation reference value. ,Will and The difference between them is used as a feedback signal input to the voltage compensator, thereby affecting... By adjusting the voltage using this secondary compensation method, the bus voltage drop caused by the droop coefficient can be reduced, effectively maintaining the stability of the system voltage.

[0167] When the improved droop control strategy taking into account SOH does not consider secondary compensation of bus voltage, the following relationship holds when the system is stable: .

[0168] After voltage compensation... The following adjustments have been made, resulting in the following relationship: .

[0169] in, For the transfer function of the secondary compensation stage, simply let: ,get .

[0170] After introducing a secondary compensation stage, the improved droop method considering SOH can overcome the bus voltage drop caused by the droop coefficient and can reduce the bus voltage... Stabilize at expected value nearby.

[0171] This scenario example also analyzes the limitations of traditional droop control strategies, particularly their shortcomings in maintaining grid voltage stability and adjusting power distribution. Next, an improved droop control strategy considering State of Harshness (SOH) is proposed. The effectiveness of the proposed strategy in improving grid voltage stability and power distribution is theoretically verified. Simulation results on the Matlab / Simulink platform demonstrate that the proposed strategy effectively combines the State of Charge (SOC) and State of Harshness (SOH) of energy storage modules. Based on the differences in SOH between modules, it enables energy storage modules with higher SOH in the parallel system to output more power and achieve a greater depth of discharge, effectively distributing power load and stabilizing the bus voltage at the ideal value. For detailed simulation verification, please refer to the following content.

[0172] In practice, a system was established in Matlab / Simulink as follows: Figure 10 The simulation model of the DC microgrid droop method shown consists of three parallel energy storage units, each containing a VFB and a bidirectional Buck / Boost converter.

[0173] Specifically, taking VFB operating in discharge mode as an example, considering the inconsistent State of Health (SOH) of each branch VFB energy storage unit, the above parallel energy storage system is simulated: ① Under the condition of inconsistent SOH, the effectiveness and rationality of the proposed strategy are verified by adopting the traditional droop control strategy and the improved droop control strategy that takes into account SOH. ② For the proposed improved droop control strategy that takes into account SOH, the influence of the power exponent n on the power balance speed is investigated. The simulation parameters are shown in Table 1.

[0174] Table 1

[0175] A comparative experiment on the SOH difference value was conducted using the traditional droop control method and the improved droop control method to verify the rationality and effectiveness of the improved droop control. Considering the difference in SOH values ​​between parallel VFB modules, [further details are needed]. , , As simulation conditions, considering system stability, the droop control coefficient K=1 is taken, and the line resistance is respectively taken as... , , Other parameters are shown in Table 1. The system was simulated using traditional droop control, and the system output current, voltage, and SOC curves are shown below. Figure 11 , Figure 12 , Figure 15 As shown in the figure. The system was simulated using improved droop control. With power exponent n=5, the system output current, voltage, and SOC curves are shown in the figure. Figure 12 , Figure 14 , Figure 16 As shown.

[0176] Depend on Figure 11 and Figure 12 It is known that traditional droop control does not consider the internal health status of the parallel energy storage system, resulting in consistent output of different parallel energy storage units in the SOH, leading to a basically constant output current value. An improved droop control strategy considering the SOH, however, increases the droop control coefficient. The initial adjustment ranges for the outputs of each module differ significantly, resulting in large differences in the output currents of the parallel modules during the early stages of discharge. After rapid adjustment, the differences in the State of Charge (SOC) of each energy storage module become smaller, but the State of Harmony (SOH) varies among the modules. Therefore, once the system stabilizes... The adjustment range of the output of each module still differs, causing the group of modules with higher SOH in the parallel modules to bear a larger output current.

[0177] Depend on Figure 13 and Figure 14 It is known that traditional droop control will cause the bus voltage to drop, which drops by 33.1V compared to the ideal value. The improved droop control taking into account the SOH takes into account the bus voltage drop and adds a voltage compensation circuit. When the parallel energy storage system is running, it can stabilize the bus voltage near the ideal value, effectively improving the stability of the system.

[0178] Depend on Figure 15 and Figure 16 It can be seen that after 15 seconds of discharge, the difference in SOC between the conventional droop control VRB energy storage unit 1 and VRB energy storage unit 3 is 0.0859. The improved droop control system reaches equilibrium after 14 seconds of discharge.

[0179] Furthermore, the simulation of the power exponent n in droop control is improved. Because... Depend on , It consists of SOH and n, where the initial droop coefficient is... Take 1, As the SOH increases from 0 to 0.2, the exponent n increases from 0.7 to 1, and the power exponent n increases from 1 to 10, from the perspective of a single energy storage module, The trend of change is as follows Figure 17 As shown.

[0180] Depend on Figure 17 It can be seen that when the power exponent n is too small, the droop coefficient... The value of n changes relatively smoothly, so this paper will use n values ​​of 5, 6, and 7 to compare the simulation results.

[0181] It should be noted that: the power exponent n is related to the droop coefficient It plays an important role; it determines The strength of the droop coefficient's influence directly affects the efficiency and speed of the system's energy distribution. Appropriately adjusting the power exponent n allows for flexible control of the droop coefficient. The size of the battery system can be adjusted to achieve faster or slower system balancing, thereby optimizing the overall performance and efficiency of the battery system.

[0182] Considering the SOH differences within the energy storage system, this application improves the influence of the power exponent in the droop control coefficient on the power balance speed, setting the SOH of the three VRB energy storage simulation units as follows: , , By changing the power exponent, the improved droop control strategy taking into account SOH was verified and analyzed. Other parameters are shown in Table 1. When the power exponent n=6, the SOC curve of the parallel energy storage system is as follows: Figure 18 As shown, when the exponent n=7, the SOC curve of the parallel energy storage system is as follows: Figure 19 As shown.

[0183] Based on the simulation results for different values ​​of the power exponent n, the balancing time of the parallel system under the improved droop method is shown in Table 2.

[0184] Table 2

[0185] As shown in Table 2, the system balancing time of the improved droop control decreases as the power exponent n increases, proving that when the power exponent n gradually increases under the premise of ensuring stable system operation, the balancing rate of the parallel system increases accordingly. After the parallel energy storage modules achieve balancing, the SOH of each parallel module still differs. Considering the different health states of each module, the SOC of each energy storage unit shows a slight difference again over time. The energy storage unit with a smaller SOH is allocated a smaller output power, and the energy storage module with a larger SOH is allocated a larger output power.

[0186] Through the above simulations, the limitations of the traditional droop control strategy were analyzed, especially its shortcomings in maintaining grid voltage stability and adjusting power distribution. Secondly, an improved droop control strategy considering SOH was proposed. The effectiveness of the proposed strategy in improving grid voltage stability and power distribution was theoretically verified. Simulation results on the Matlab / Simulink platform showed that the proposed strategy effectively combines the SOC and SOH of the energy storage modules. Based on the differences in SOH between modules, it can enable energy storage modules with larger SOH in the parallel system to output more power and have a greater depth of discharge, effectively distributing power load and stabilizing the bus voltage at the ideal value.

[0187] The above scenario examples verify the coordinated control method of the flow battery energy storage system provided in this specification. Through purely localized control logic, it can effectively solve the problems of communication dependence and poor voltage quality in traditional methods.

[0188] While this specification provides the steps of operation for the methods described in the embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. The terms "first," "second," etc., are used to denote names and do not indicate any particular order.

[0189] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0190] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer-readable storage media, including storage devices.

[0191] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of this specification.

[0192] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. This specification can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0193] Although this specification has been described by way of examples, those skilled in the art will recognize that many variations and modifications are possible without departing from the spirit of this specification, and it is intended that the appended text include such variations and modifications without departing from the spirit of this specification.

Claims

1. A method of coordinated control of a flow battery energy storage system, the method comprising: The method is applied to a flow battery energy storage system, wherein the flow battery energy storage system includes at least: multiple flow battery energy storage units, the multiple flow battery energy storage units being connected in parallel to a common DC bus via a bidirectional DC / DC converter, the method comprising: When the preset triggering conditions are met, the health status and state of charge values ​​of multiple flow battery energy storage units are collected. The average state of charge (SOC) of the system is determined based on the SOC values ​​of multiple flow battery energy storage units; and the SOC difference of the multiple flow battery energy storage units is calculated based on the SOC values ​​of multiple flow battery energy storage units and the average SOC of the system. Based on the differences in state of charge and health status of multiple flow battery energy storage units, the improved droop coefficients of multiple flow battery energy storage units are determined. The output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system are adjusted based on the improved droop factor of multiple flow battery energy storage units.

2. The method of claim 1, wherein, The step of determining the improved droop coefficient for multiple flow battery energy storage units based on the differences in their state of charge and health status includes: Acquire the current time period's status parameter monitoring records of the flow battery energy storage system; Based on the status parameter monitoring records for the current time period, determine the system power fluctuation parameters and the system state of charge mean fluctuation parameters for the current time period; Based on the preset mapping relationship, the influence intensity parameters that match the system power fluctuation parameters and the system state of charge mean fluctuation parameters for the current time period are determined. Based on the influence intensity parameter, the state of charge difference value of multiple flow battery energy storage units, and the health status value, the improved droop coefficient of multiple flow battery energy storage units is determined.

3. The method of claim 2, wherein, The step of determining the improved droop coefficient for multiple flow battery energy storage units based on the influence intensity parameter, the state of charge difference value of multiple flow battery energy storage units, and the health state value includes: The droop factor for the current improved flow battery energy storage unit is determined using the following formula: wherein, is an improved droop coefficient for the current flow battery energy storage unit, is an initial droop coefficient, SOH is a state of health value for the current flow battery energy storage unit, SOC is a state of charge value for the current flow battery energy storage unit, n is an impact strength parameter, is a state of charge difference value for the current flow battery energy storage unit.

4. The method according to claim 1, characterized in that, The step of adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of multiple flow battery energy storage units includes: Obtain the wire type, temperature parameters, and heat dissipation coefficient of multiple flow battery energy storage units; Based on the conductor type, temperature parameters, and heat dissipation coefficient of multiple flow battery energy storage units, the line impedance of multiple flow battery energy storage units is determined. Based on the improved droop coefficient and line impedance of multiple flow battery energy storage units, a matching target regulation ratio is determined. Adjust the output current and / or output voltage of the flow battery energy storage unit in the flow battery energy storage system according to the target adjustment ratio.

5. The method according to claim 4, characterized in that, When the flow battery energy storage system includes at least a first flow battery energy storage unit and a second flow battery energy storage unit, determining the matching target adjustment ratio based on the improved droop coefficient and line impedance of the multiple flow battery energy storage units includes: The target adjustment ratio is determined according to the following formula: in, The target adjustment ratio is based on the current. This is the output current of the first flow battery energy storage unit. This is the output current of the second flow battery energy storage unit. The improved droop factor for the first flow battery energy storage unit. The improved droop factor for the second flow battery energy storage unit. The line impedance of the first flow battery energy storage unit is given. The line impedance is the second flow battery energy storage unit.

6. The method according to claim 1, characterized in that, The method of adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on the improved droop coefficient of multiple flow battery energy storage units further includes: Obtain the current output voltage of multiple flow battery energy storage units; The reference voltage of multiple flow battery energy storage units is calculated based on the improved droop factor of multiple flow battery energy storage units. Based on the current output voltage and reference voltage of multiple flow battery energy storage units, determine the first voltage compensation signal for multiple flow battery energy storage units; The output voltage of the flow battery energy storage unit in the flow battery energy storage system is adjusted based on the first voltage compensation signal of multiple flow battery energy storage units.

7. The method according to claim 1, characterized in that, After adjusting the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system according to an improved droop coefficient of multiple flow battery energy storage units, the method further includes: Get the current voltage of the common DC bus; The second voltage compensation signal is determined based on the current voltage of the common DC bus and the preset voltage compensation reference value. The output voltage of the flow battery energy storage unit in the flow battery energy storage system is adjusted according to the second voltage compensation signal.

8. The method according to claim 1, characterized in that, The flow battery energy storage unit includes an energy storage unit based on a vanadium redox flow battery.

9. A coordinated control device for a flow battery energy storage system, characterized in that, An application is made in a flow battery energy storage system, wherein the flow battery energy storage system comprises at least: multiple flow battery energy storage units, the multiple flow battery energy storage units being connected in parallel to a common DC bus via a bidirectional DC / DC converter, the device comprising: The acquisition module is used to acquire the health status and state of charge values ​​of multiple flow battery energy storage units when preset trigger conditions are met. The first determining module is used to determine the average state of charge of the system based on the state of charge values ​​of multiple flow battery energy storage units; and to calculate the state of charge difference value of multiple flow battery energy storage units based on the state of charge values ​​of multiple flow battery energy storage units and the average state of charge of the system. The second determining module is used to determine the improved droop coefficient of multiple flow battery energy storage units based on the difference in state of charge and the health status value of multiple flow battery energy storage units. An adjustment module is used to adjust the output current and / or output voltage of the flow battery energy storage units in the flow battery energy storage system based on an improved droop coefficient of multiple flow battery energy storage units.

10. An electronic device, characterized in that, It includes a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, It stores computer instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 8.

12. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

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